Technological Integration for Micronutrient Monitoring in Water Systems

Technological Integration for Micronutrient Monitoring in Water Systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Technological Integration for Micronutrient Monitoring in Water Systems M.K. Gule, R. Cossa, P. Khowa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7335820/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The convergence of Internet of Things (IoT) and cloud computing offers a transformative approach to micronutrient monitoring in environmental and agricultural systems. As IoT devices generate continuous data streams, cloud platforms provide scalable resources for real-time processing, analysis, and storage. This systematic review, conducted under PRISMA 2020 guidelines, examined 36 studies on IoT–cloud integration for micronutrient detection. Most studies were sourced from Google Scholar (50.00%), Web of Science (33.33%), and SCOPUS (16.67%). Peer-reviewed journal articles dominated (72.22%), with Asia contributing the highest share of research (50.00%), led by India (30.56%). Surface water was the most monitored source (38.89%), followed by treated water (19.44%) and groundwater (13.89%). Chemical parameter sensors were most common (43.90%), and Arduino platforms were the predominant hardware (52.78%), with GSM communication technologies leading (46.43%). Unspecified cloud platforms accounted for 25.00%, while AI-enhanced cloud solutions represented 14.29%. Core challenges identified include data volume, energy constraints, latency, interoperability, and security vulnerabilities, particularly in remote settings. The findings highlight the need for robust, context-aware IoT–cloud frameworks, improved reporting standards, and the adoption of AI and edge–cloud architectures to enhance sustainable, data-driven decision-making in precision micronutrient management. Electrochemistry Marine and Freshwater Ecology Marine and Freshwater Biology Electrical Engineering Internet of Things (IoT) Cloud Computing Micronutrient Monitoring Environmental Sensing Precision Agriculture Data Integration Edge Computing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Introduction Micronutrients are a very important substance and are needed for humans to stay healthy. They basically vitamins and minerals that are found in small amounts and are needed by the body to produce energy and to do regular bodily functions like boost the immune system, help produce blood or maintain bone health (WHO, 2022). Micronutrient deficiency is one of the major problems in the world, were hundreds of millions of people suffer from this. Micronutrient deficiency is defined as a lack of essential vitamins and minerals that are required in small amounts by the body for proper growth and development (WHO, 2022). To the people with lack of micronutrients, this could lead to serious health problems, affecting not only physical but mental health too. But micronutrients are not just found in the human body or essential for the human body only, they are also found in foods and the soil, such as copper, manganese, chlorine, boron and zinc to name a few (D. KUMAR, 2022). They are also crucial for a balanced health crop growth, like flowering, root development and fruit production. Even though they are found in very small amounts, if the soil is micronutrient deficient it could lead to decreased crop yield and overall plant productivity (D. KUMAR, 2022). Traditional ways of monitoring micronutrients are not economical and are delayed, even if they were accurate (M. BELGIU, 2023). They did not offer remote sensing and real-time monitoring systems, which is not ideal especially in this modern-day age of advanced technology, where a lot of things are going digital. All of the issue mentioned above are the reasons why there is a lot of research and interests in micronutrients monitoring systems. Theres an abundance of new advanced technologies that could be used and/or integrated with the traditional methods of tracking micronutrients to make better systems for monitoring micronutrients. Some the ways can be of integration with (IoTs) Internet of Things, sensors and cloud computing (S. AHMED, 2023). The IoT are basically a include network of systems embedded with technology such as sensors and software and can include mechanical and digital machines and consumer objects (S. AHMED, 2023). This technology can help us achieve better monitoring of nutrients in real time using IoTs and its sensors to detect micronutrients and send real time monitored data to our device remotely to show use the results. They usually use strong sensitive sensors that can even detect the slightest change in micronutrients, as micronutrients are normally found in very small quantities in the soil (A. ANJALI, 2024). The data recorded by the sensors will then be stored, analysed and interpreted by the cloud-computing section of the system, via processing and machine learning techniques, the same data will also be shown on the display of a digital device, for users to access anytime. The IoT and cloud computers detect, upload, analyse, and present micronutrient metrics automatically with minimal human intervention (A. ANJALI, 2024). The proposed system is very good idea, but in certain instances it is not favourable. Some of the issues faced with these systems are network/connection problems, integration with other systems/devices and sensor failures (S. AHMED, 2023). Network connectivity, especially in rural areas or in times or extreme environments conditions like heavy rain and strong winds, tends to be weak, or in times of power outages like load shedding, wireless networks are disrupted. There are also cloud storage and security problems. Cloud storage can get expensive if large amounts of data need to be stored, also on the issue of security and privacy concerns, where the system can be hacked and user data that is sensitive can be stolen (Staff, 2025). Another issue that can arise is the stubbornness of other people who might not be willing to change the traditional ways of doing things that they are used to, as they are comfortable with familiarity. The are a lot of opportunities for future growth of these systems as we make breakthroughs in technology advancements in the world. We can use the real time monitoring capabilities of the system to monitor and check health of people and not just for soil and plant applications. This will help people to manage and take care of their personal health easier, by providing personalized nutrition management, prevent diet-related health problems and remind patients to take their medicine in time for example. The real time monitoring will also help improve and promote sustainable agricultural practices in the industries of crop production. Integration with AI and machine learning will help enhance the systems automatic functioning capabilities, allowing for more accurate and precise readings of micronutrients (Umar Farouk Mustapha, 2021). Lastly the use of cloud can be made more secure by building firewalls and better access credential login details of customers to prevent privacy concerns. Table 1 Comparative Analysis of the Existing Review Works and Proposed Systematic Review on IoT and Cloud Computing Issues, Challenges, and Opportunities in Micronutrient Monitoring. Ref. Cites Year Contribution Pros Cons Bhati, A.2021 485 2021 Explores integration of IoT with Cloud Computing, highlights challenges (security, data management, scalability) Enables efficient data storage and processing for IoT and Supports scalability for IIoT data. Inconclusive results on integration and lacks concrete implementation models Alshami, E.2024 1 2024 Proposes a real-time multi-nutrient water quality monitoring system using IoT sensors to predict nutrient levels like N, P, K, pH, BOD Accurate predictions (90% accuracy), Real-time monitoring, Combines AI + IoT for efficiency & Improves decision-making in precision agriculture Limited data availability may affect model training; Environmental factors can reduce sensor accuracy & Ensemble models may be computationally intensive Adebayo, A.2025 47 2025 Comprehensive review of smart sensors used for continuous in-line environmental and water quality monitoring using IoT and cloud services Enables real-time, autonomous monitoring, Integrates wireless communication and cloud storage , Reduces cost of frequent lab-based testing. Energy consumption challenges, High initial cost of deployment, Reliability and useful life of wireless systems need improvement. M. M. Sadeeq,2021 22 2021 Developed an IoT-based water quality monitoring system using NB-IoT and Arduino, integrated with machine learning models (DT, RF, SVM, NN, GB). Real-time monitoring via mobile/web, Low-cost and self-assembled sensors and SMS alerts via LINE. Data limited to Tunghai campus, Limited scope of contaminants (no E. coli, chlorine, HABs yet). D. Kadam,2023 108 2023 Conducted an extensive review (2011–2020) of IoT-based water quality monitoring in aquaculture. Categorized research by environment, approach, parameters, and solutions. Prioritizes key water parameters (temp, DO, pH), Supports Aquaculture 4.0 development& Encourages use of smart sensors and automation. Limited focus on marine aquaculture (19%) and Emphasizes need for long-term practical research. A. Botta,2016 2 2016 Proposed a novel, low-cost, low-power IoT-based water quality monitoring system for traditional distribution networks using sensors and machine learning for real-time analysis. Real-time water quality prediction via ML, Low-cost, solar-powered, low-energy sensors and Effective in rural areas Performance dependent on network/cloud availability S. A. Albahri et al,2024 29 2024 Conducted a systematic review on IoT innovations in water and wastewater management, focusing on wireless technologies, sensor integration, and communication protocols. Real-time monitoring; use of multiple sensors (pH, flow, level); enhanced efficiency with Sigfox, Zigbee, MQTT; improved decision-making; automation and scalability Challenges include sensor accuracy, energy optimization, communication reliability, limited interdisciplinary collaboration R. Selvanaran,2024 0 2024 Presented a comprehensive review on the use of sensors, IoT, and drones for real-time, adaptive water quality monitoring. Enables real-time, high-resolution data acquisition, Uses AI and cloud for predictive modelling and Drones improve accessibility and coverage High implementation and maintenance costs, Cybersecurity vulnerabilities and Lack of standardization for integration. We also identify, in our systematic review, some of the most egregious research gaps on the topic of applications of Internet of Things (IoT) and cloud computing technology to micronutrient monitoring. They not only tell us where already there is knowledge gap but also provide useful pointers to guide future research so that such technologies are realistically better achieved and scaled. Its individual application requirements of sensitivity, timely aggregating the data, and ambient condition versatility of were installed give way, too often, to more vague appeals. Even some papers focus more on situating sensor functions and technical specifics rather than discussing the implementation complications that micronutrient deficiency occurs at most at the rural or lower-income areas. Yet interesting as both the technologies are as monitors of right monitoring, plans are more sketched independently of each other and not together collectively as an effort to consider both their implications on rightful data, action as much as policy, and final nutritional impact of a project. In addition, existing literature to date relies on either pilot data or test-controlled environments, which constrain the degree to which scalability over the longer term, sus-stainability across systems, and adoption in the field can be achieved. There is still a pressing need for longitudinal and field research into the longer-term effect of these technologies across various geographies and socio-economic contexts. 1.1 Research questions Although IoT and cloud computing are increasingly applied in healthcare and agriculture, their use in micronutrient monitoring is still limited. Most studies focus on the individual technologies or general nutrient monitoring, without assessing how IoT–cloud integration affects the validity, usability, and responsiveness of micronutrient data. This review addresses the following research questions: Why do recent studies emphasise integration aspects such as data validity, user accessibility, and latency, rather than cloud infrastructure and sensor deployment? How can IoT-based monitoring systems be scaled to provide affordable, precise, real-time micronutrient information for vulnerable populations? What are the current applications of cloud computing for storage optimisation, analysis, and decision-making in micronutrient programmes? How are AI-enabled analytics and smart alert functionalities being incorporated into micronutrient monitoring systems? How are IoT–cloud systems adapted to different geographic and socio-economic contexts? 1.2 Hypotheses development The review is based on the hypothesis that integrating IoT and cloud computing improves micronutrient monitoring by enabling accurate, real-time measurements and data processing, despite connectivity and security challenges. The following hypotheses are examined: H1: Methodological inconsistencies, lack of standardised reporting frameworks, and varied study designs contribute to underreporting in micronutrient research. H2: IoT–cloud integration enables accurate, real-time micronutrient monitoring. H3: Combining IoT–cloud systems with machine learning improves monitoring efficiency and analysis. H4: Cloud-based business intelligence models support micronutrient monitoring by enabling real-time data handling and analysis in health and agriculture. 1.3 Rationale This review evaluates the use of IoT and cloud computing in micronutrient monitoring, focusing on data collection, processing, and storage. These systems have been applied in varied geographic and economic contexts, with adoption influenced by infrastructure and technical capacity. Challenges include system integration, connectivity, and data privacy. The review examines literature from 2014–2025 to summarise how IoT–cloud approaches are used in agricultural and health-related micronutrient monitoring. 1.4 Objectives The objectives are to: Document current applications of IoT and cloud computing in micronutrient monitoring. Identify technical and operational challenges to adoption. Describe the technologies and architectures currently used. Summarise the main approaches reported in the literature between 2014 and 2025. 1.5 Research contributions This review: Identifies IoT–cloud approaches used for real-time micronutrient monitoring in water systems. Summarises technical and infrastructural challenges affecting system adoption and scale-up. Outlines areas where AI, edge–cloud systems, and low-power sensing are already being applied in nutrient monitoring. Highlights gaps in the literature, including underreporting of technical details such as sensor types and cloud platforms. 1.6 Research novelty To date, no systematic review has specifically examined IoT–cloud integration for micronutrient monitoring. Existing studies generally focus on broader agricultural or healthcare contexts. This review is novel in its focus on micronutrient-specific applications, providing: An analysis of technical, operational, and contextual issues in adopting IoT–cloud monitoring systems for micronutrients. A synthesis of reported technologies and system architectures used between 2014 and 2025. Materials and Methods This systematic review follows the guidelines of the Preferred Reporting Item for Systematic Reviews and Meta Analysis (PRISMA). In this subsection, the study outlines the methodology employed to conduct a systematic review focusing on the issues, challenges and opportunities in IoT and cloud computing integrated systems in micronutrients monitoring. The study is based on a review of literature published over the last decade, from 2014 to 2025. The review focuses on peer reviewed studies found from online database like Google Scholar, Web of Science and SCOPUS. The studies are searched used a search string tailored specifically to the type of online database. These resources are chosen because of their reliability, credibility and reputation in the research field. 2.1. Eligibility criteria A systematic study of all peer-reviewed and published research works that focuses on the integration of IoT and cloud computing in micronutrients monitoring systems. Only research works published in English between 2014 and 2025 were included in the analysis. A proper criterion for inclusion was adapted to ensure the inclusion of research papers that specifically focus on studies that concern and address the challenges, issues and opportunities related to the application of IoT and cloud computing integrated systems, that monitor micronutrient levels in water, soil, agriculture and healthcare. The inclusion and exclusion criteria for this study are tabulated as in Table 2 (Khanyi et al., 2024 ; Sofianopoulos et al., 2025 ; Xiao et al., 2025; Skosana et al., 2024 ; Yu et al., 2025 ; Aldossary et al., 2025 ; Jørgensen & Ma, 2025 ; Mtjilibe et al., 2024; Mishra et al., 2025 ; Ahoa et al., 2025; Bankó et al., 2025 ; Thobejane & Thango, 2024 ; Fernando & Lăzăroiu, 2024 ; Andriulo et al., 2024; Danil et al., 2025; Marques et al., 2025 ; Thango & Obokoh, 2024 ; Paraskevas et al., 2024 ; Cacciuttolo et al., 2024 ). . Table 2. Proposed Inclusion and Exclusion Criteria. Criteria Inclusion Exclusion Topic Article papers focusing on issues, challenges or opportunities of IoT and cloud computing systems in micronutrients monitoring Article that do not focus on addressing micronutrient monitoring nor focus on IoT and cloud computing platforms/systems Research Framework The Articles must include research framework or methodology or methodology for applications related to IoT and cloud computing systems in micronutrients monitoring Articles must exclude research framework or methodology that are not related IoT and cloud computing systems in micronutrients monitoring Language Must be written in English Articles published in languages other than English Period Articles between 2014 to 2025 Articles outside 2014 and 2025 2.2. Information sources The data for this review was gathered from a range of peer-reviewed journal articles, conference proceedings, and technical papers published between 2014 and 2025. Primary sources included established academic databases such as SCOPUS, Web of Science, and Google Scholar, ensuring high academic quality and relevance. The selected studies focused on the intersection of Internet of Things (IoT) and Cloud Computing in the context of micronutrient monitoring and water quality analysis. Table 2 outlines the databases consulted and highlights their relevance to the review’s scope and inclusion criteria (Khanyi et al., 2024 ; Sofianopoulos et al., 2025 ; Xiao et al., 2025; Skosana et al., 2024 ; Yu et al., 2025 ; Aldossary et al., 2025 ; Jørgensen & Ma, 2025 ; Mtjilibe et al., 2024; Mishra et al., 2025 ; Ahoa et al., 2025; Bankó et al., 2025 ; Thobejane & Thango, 2024 ; Fernando & Lăzăroiu, 2024 ; Andriulo et al., 2024; Danil et al., 2025; Marques et al., 2025 ; Thango & Obokoh, 2024 ; Paraskevas et al., 2024 ; Cacciuttolo et al., 2024 ). 2.3. Search strategies The data in sheer spirit of this review, literature has been searched in high-impact research bibliographic scholarly databases with a predisposition towards research combining IoT and cloud computing and its adoption in micronutrient monitoring in medicine and agriculture. Every effort has been made to adopt technological topics and technologies as and when possible, in applications like system design, data handling, and deployment-related issues in field deployment. Systematic searching of the first three repositories, Google Scholar, Scopus, and Web of Science, was conducted. A search keyword was given and used to search for: ((((((("Internet of Things" OR "IoT") AND ("Cloud Computing") AND ("Micronutrient Monitoring" OR "Micronutrient Tracking") AND ("Agriculture" OR "Public Health") AND ("Real-time Data" OR "Data Management" OR "Remote Sensing")) These words were employed in a search effort for research on IoT and cloud technology adoption for micronutrient status monitoring, especially in low-resource or rural environments. Literature review was limited to 2014–2025 to cite current technological development. The initial search in Google Scholar produced 35 articles, 33 in Scopus, and 25 in Web of Science. All articles were screened for relevance, and research articles only that were pertinent to the purpose of the study were synthesized in final analysis. This stage offered a solid and shared platform to deliberate on the research problem and viability of applying IoT and cloud computing in tracking micronutrients. See Table 3 below. Table 3 Results Achieved from Literature Search. No. Online Repository Number of results 1 Google Scholar 35 2 Web of Science 24 3 Scopus 31 Total 90 2.4. Selection process The screening and selection of relevant literature followed a structured and systematic approach to ensure both the quality and relevance of the studies included in this review. Articles were initially retrieved from three reputable databases: SCOPUS, Web of Science, and Google Scholar. All records were exported into Microsoft Excel for proper organization, removal of duplicate entries, and tracking of inclusion/exclusion decisions. The inclusion criteria specified that articles must explicitly address IoT and cloud computing technologies in the context of micronutrient monitoring, water quality analysis, or micronutrient contamination detection. Eligible studies were required to be published between 2015 and 2025, be written in English, and include a clear methodology involving the application or evaluation of IoT-cloud systems in micronutrient or environmental monitoring. Title and Abstract Screening: Each article’s title and abstract were independently reviewed to preliminarily assess relevance to the review objectives. Full-Text Screening: Studies that met the initial screening criteria were further subjected to a comprehensive full-text review. Each article was evaluated by at least two independent reviewers, and disagreements were resolved by the third reviewer through consensus (Khanyi et al., 2024 ; Sofianopoulos et al., 2025 ; Xiao et al., 2025; Skosana et al., 2024 ; Yu et al., 2025 ; Aldossary et al., 2025 ; Jørgensen & Ma, 2025 ; Mtjilibe et al., 2024; Mishra et al., 2025 ; Ahoa et al., 2025; Bankó et al., 2025 ; Thobejane & Thango, 2024 ; Fernando & Lăzăroiu, 2024 ; Andriulo et al., 2024; Danil et al., 2025; Marques et al., 2025 ; Thango & Obokoh, 2024 ; Paraskevas et al., 2024 ; Cacciuttolo et al., 2024 ). . 2.5. Data collection process The data collection process was conducted using a standardized approach to ensure accuracy, consistency, and transparency (Khanyi et al., 2024 ; Sofianopoulos et al., 2025 ; Xiao et al., 2025; Skosana et al., 2024 ; Yu et al., 2025 ; Aldossary et al., 2025 ; Jørgensen & Ma, 2025 ; Mtjilibe et al., 2024; Mishra et al., 2025 ; Ahoa et al., 2025; Bankó et al., 2025 ; Thobejane & Thango, 2024 ; Fernando & Lăzăroiu, 2024 ; Andriulo et al., 2024; Danil et al., 2025; Marques et al., 2025 ; Thango & Obokoh, 2024 ; Paraskevas et al., 2024 ; Cacciuttolo et al., 2024 ). A structured data extraction form was developed in Microsoft Excel to capture all relevant information, including study title, year, author(s), study objectives, methodology, IoT and cloud computing technologies used, types of micronutrients monitored, key findings, and limitations. Each included article was reviewed by two independent reviewers who extracted data separately to reduce the risk of bias or errors. Any discrepancies between the two reviewers were resolved through discussion, and if consensus could not be reached, a third reviewer acted as an arbitrator. No automation tools were used in the data extraction phase; all information was collected manually to preserve context and ensure a comprehensive understanding of each study. Where key data were missing or unclear, attempts were made to contact the original authors via email to request clarification or supplementary information. This process ensured the completeness and accuracy of the dataset used in the synthesis and analysis stages of the review. 2.6. Data items This section provides a comprehensive overview of the data items sought in this systematic review, focusing on searching papers across different database and analysing the data to look for specific outcomes, related to the use of IoT and cloud computing devices for micronutrient monitoring. 2.6.1 Data Collection Method The data collection methods include different parts of an integrated system designed to find a specific outcome that measure and indicate micronutrient content in a substance. The IoT device will use its sensors to detect micronutrient contents. It will also offer real time analysis of user health in devices like mobile phones or wearables smart watches, which will give the user information about their dietary needs, using data specifically catered to them like health goals, diet needs and restrictions. The cloud data processor stores and analyses the data from sensors to make sure the recommendations given to the user matches their needs and indicates inconsistencies. Cloud based platforms like AWS IoT or Azure IoT will be used for security measures, which will make sure of secured data storage and secured transmission of data from one database to another, with real-time monitoring capabilities. Software for Edge or Cloud processing will be used to make sure that correct and clean data is processed and produced. Hybrid integration of IoT devices with cloud computers will be and integration of manual user input with automated sensor data processing. 2.6.2. Definition of collected data variables This review identified and extracted several critical variables from the included studies to evaluate the technological, operational, and contextual dimensions of IoT and cloud computing systems applied in micronutrient monitoring, especially in water quality environments. These variables were selected based on their direct relevance to deployment characteristics, data communication strategies, challenges, and innovation potential in the field. Table 4 Data Variables Collected. Field Description Study characteristics Deployment region, monitoring environment, sample size, IoT/cloud architecture, and communication protocol. Intervention characteristics Includes key performance metrics (sensor accuracy, data latency, uptime, energy use). Economic factors Includes the investment valuation of IoT/cloud deployments, the competitive advantages gained, and the overall ROI. External influences Includes the vendor ecosystem, adoption and demand trends, analytics-algorithm updates, emerging technological innovations, and environment‐specific monitoring drivers. 2.7. Study risk of bias assessment Risk of bias was assessed independently by four reviewers to prevent bias. Disagreements' resolutions were achieved through discussion, and any persisting disagreements were referred to a fourth reviewer to make the final decision. Where research appeared not to have taken tidily well-constructed methodological data with it—where, say, off-the-shelf cloud platforms or IoT devices had been employed a secondary cross-matching follow-up search in Web of Science, Scopus, and Google Scholar was thus performed. Additional manual online repository searching was also carried out in the mindset of not wanting to be in any way biased so that one obtains the best that there is and most comprehensive review. No automated procedure has been used here. 2.8. Synthesis methods The following flowchart illustrates the scientific methodology applied in our literature review on the use of cloud computing and IoT applications to monitor micronutrients. It begins with Study Selection, where concerned studies are searched and screened against previously defined a priori eligibility criteria. It then goes to Data Standardization in pre-processing data by cleaning data and data transformation in an attempt to make data homogenous from heterogenous sources. In Data Analysis, data is presented in tables and figures and primary analysis is done. Then the flow moves to Heterogeneity Assessment, where we evaluate heterogeneities between studies by sensitivity analysis and subgroup. Finally, Bias Assessment is done to monitor possible biases and maintain transparency of review. Following this sequential flow makes sure proper and accurate integration of the research that dominates the field is done. In this cloud computing- and IoT-based micronutrient monitoring systematic review, we employed strict synthesis protocols where our results will be transparent, valid, and reproducible. In deciding whether to include or exclude a study for synthesis, we properly documented all study data and checked it against our pre-specified inclusion criteria. This was done in an attempt to include within the review the most valid and also the most methodologically sound studies Missing or missing data were addressed through imputation methods and periodic data cleaning to pre-specified comparability ahead of time among studies. Data were quantified through tabular summary and plots to enable us to publish effect estimates and confidence intervals without restriction and identify trends or outliers. Random-effects meta-analysis model was used to pool after monitoring practice, technology, and settings adjustment. Subgroup analysis was also performed to examine the effect of geography, extent of monitoring coverage, and whether the system is IoT- or cloud-based in nature. Subgroup and meta-regression analysis enabled us to control for the likely causes of heterogeneity and again to further clarify the degree to which each contextual parameter is driving the performance of IoT and cloud-based systems for micronutrient monitoring. For the purposes of maintaining reliability stability to our results, sensitivity analysis was carried out to pre-estimate half study exclusion or alteration of the analysis models on our results. Systematic method enabled us to develop evidence-based results that would guide policy-makers, practitioners, and researchers to an effort for improvement in nutrition ssurveillance through specific new technological improvement. 2.8.1. Eligibility for Synthesis To determine eligibility for inclusion in the synthesis, each study was systematically assessed based on its alignment with the core themes of the review: the integration of IoT technologies, the application of cloud computing, and their combined role in micronutrient monitoring or water quality assessment. The eligibility process involved extracting key study characteristics such as the specific micronutrient or water quality parameter monitored (nitrate, calcium, iron), the type and architecture of IoT and cloud systems employed, the monitoring environment (such as agricultural fields or aquatic ecosystems), and the documented issues, challenges, or opportunities identified in the study. Studies were grouped into thematic categories that reflected their primary contributions such as technical limitations, deployment strategies, data accuracy, or scalability solutions to ensure a coherent synthesis. Only studies that presented original findings, a clear methodology, and direct relevance to the intersection of IoT, cloud computing, and micronutrient monitoring were retained for the final synthesis. 2.8.2. Data Preparation for Synthesis In this review, the methods used involved converting or standardizing data collected from various studies to ensure consistency before synthesis. For example, when effect sizes were reported differently across studies, algebraic manipulations were employed to convert these into a uniform scale, such as converting odds ratios to risk ratios where appropriate. Additionally, handling missing data was a critical aspect of the analysis. Missing summary statistics, such as standard deviations or effect sizes, were imputed using established statistical methods like multiple imputation. This approach ensured that the dataset was comprehensive and robust, allowing for a more accurate and reliable analysis (Khanyi et al., 2024 ; Sofianopoulos et al., 2025 ; Xiao et al., 2025; Skosana et al., 2024 ; Yu et al., 2025 ; Aldossary et al., 2025 ; Jørgensen & Ma, 2025 ; Mtjilibe et al., 2024; Mishra et al., 2025 ; Ahoa et al., 2025; Bankó et al., 2025 ; Thobejane & Thango, 2024 ; Fernando & Lăzăroiu, 2024 ; Andriulo et al., 2024; Danil et al., 2025; Marques et al., 2025 ; Thango & Obokoh, 2024 ; Paraskevas et al., 2024 ; Cacciuttolo et al., 2024 ). 2.8.3. Tabulation and Visual Display of Results Synthesize the results from the selected studies, a narrative synthesis approach was employed, which involved categorizing the studies based on common themes and outcomes. This approach allowed for a comprehensive comparison of the various IoT and cloud computing frameworks used in micronutrient monitoring (Khanyi et al., 2024 ; Sofianopoulos et al., 2025 ; Xiao et al., 2025; Skosana et al., 2024 ; Yu et al., 2025 ; Aldossary et al., 2025 ; Jørgensen & Ma, 2025 ; Mtjilibe et al., 2024; Mishra et al., 2025 ; Ahoa et al., 2025; Bankó et al., 2025 ; Thobejane & Thango, 2024 ; Fernando & Lăzăroiu, 2024 ; Andriulo et al., 2024; Danil et al., 2025; Marques et al., 2025 ; Thango & Obokoh, 2024 ; Paraskevas et al., 2024 ; Cacciuttolo et al., 2024 ). The synthesis was structured around the major findings, focusing on the technological challenges, solutions, and opportunities identified across studies. For studies that included quantitative data, the synthesis utilized descriptive statistics to summarize key metrics such as accuracy, efficiency, and scalability of IoT systems in micronutrient detection. In cases where meta-analysis was not feasible due to heterogeneity in methodologies or outcomes, the findings were aggregated narratively, emphasizing common trends and discrepancies. The synthesis was further informed by identifying patterns in sensor types, cloud integration methods, and the impact of various environmental factors on the monitoring systems. Where applicable, the synthesis included a comparative analysis of the effectiveness of different approaches in specific use cases, highlighting strengths and weaknesses across different sensor technologies and IoT architectures. 2.8.4. Synthesis of Results During our manual search on online repositories such as Google Scholar, Scopus, and Web of Science, we carefully reviewed and synthesized the results of relevant studies. The approach to data synthesis was guided by the nature of the data and the degree of variability observed across studies. Based on the findings from our search, we manually assessed the applicability of both fixed-effects and random-effects models, depending on the level of heterogeneity among study results. The selection of the model was determined by the characteristics of the data and our assumptions about the consistency of effects across studies. After exporting the data to Excel, we created charts to visually inspect the data, allowing us to identify patterns of variability and potential heterogeneity across the studies. This initial visual inspection provided an overview of how study results differed from one another, facilitating a more nuanced analysis. 2.8.5. Exploring Causes of Heterogeneity To analyse heterogeneity explanations across studies for cloud computing and IoT in micronutrient monitoring, subgroup analysis and meta-regression were employed. These included heterogeneity in setting, intervention as type of IoT or cloud computing utilized, monitoring systems, and geographic or environmental settings. Controlled were variables such as the nature of the nutrient being measured, scale of deployment (national or community), and local infrastructure capability in the effort to quantify their impact on successful deployment as well as data reliability. It aimed to establish patterns and indeed correlations of outcome variation towards enhancing our understanding of how these technologies under varying conditions. 2.8.6 Synthesis methods The strength of the findings of the syntheses under different assumptions and choices of methodology carried out in the review was analysed using sensitivity analyses. These covered testing the removal of high-risk bias studies, as well as the application of alternative statistical models, to minimize the over-effect of specific studies or analytical preferences on conclusions drawn. This approach helped prove the validity and reliability of findings by rendering known sources of bias operational and replicability of outcomes across different settings of analysis. 2.9. Synthesis methods In the course of conducting our systematic review of IoT and cloud computing technology integration and impact on micronutrient monitoring, it was essential that we set the risk of bias for missing data or selectively reported data. Reporting biases like selective outcomes reporting and selective publication have very significant implications to the completeness and validity of our synthesis. It is owing to this that we applied a tight and methodologically conscientious strategy in managing these threats. To identify and correct likely bias, we used traditional statistical as well as graph-based techniques. Specifically, contour-enhanced funnel plots were used as a graph diagnostic statistic for asymmetry tests to investigate publication bias. The plots provided visual proof of whether missing studies would be the result of random variation or reflective of systematic bias, where IoT deployment or cloud interventions have been underreported because of non-significant or inconclusive findings. Along with developing new tools, we emphasized utilizing established, literature-supported techniques. Adding contours to funnel plots was an effective way of showing study distribution and accuracy so that our synthesis could bridge gaps and inconsistencies between data sources. Subjectivity bias was also minimized with effort directed towards duplicate assessment by multiple reviewers. There were also discrepancies in assessment, which were agreed and resolved either by consensus or later guidance from an expert in systematic review methodology. This joint exercise enhanced the independence and strength of our study. We deliberately avoided using bias estimation reporting automation tooling for this review. A human approach—typing information into software like Excel for data visualization and plotting—offered an intimate review of the dataset. The human approach allowed us to spot fine trends and fine whispers of bias, particularly in the niche field of micronutrient tracking with IoT and cloud networks. To further enhance the comprehensiveness of our synthesis, hand searches were performed across a range of academic databases including Google Scholar, Scopus, and Web of Science. Through source triangulation, thorough cross-referencing of the studies and allowing validation for consistency was facilitated in our extraction process and interpretation. Based on the peculiar topology of cloud computing and IoT research, especially nutritional health monitoring, we adapted conventional bias measures to suit the peculiarity of such research. Outputs in such research are also presented differently from those in social science or clinical research, and methodology modifications were required for context validity. The adaptation to suit our needs made our synthesis informative and rigorous. To bring comfort to the practices of reproducibility and openness, we have detailed all of the methodologies and analysis choices made during this evaluation, which are included within the supplementary material. Such openness does not only add validity to our results but also assists coming research to replicate, validate, or build upon our study—thus aiding in research advancement in the implementation of IoT and cloud computing in micronutrient surveillance. 2.10. Reporting Bias The analysis of sample characteristics across the reviewed studies reveals a strong focus on IoT and micronutrients monitoring systems. During the compilation of information when writing this literature study, the PRISMA guidelines were followed throughout, to try not being bias when selecting the reports to use and avoid being bias from selectively reported results. Any paper that is related micronutrients monitoring, IoT and cloud computing will be considered and reported. After reporting and compilation of these reports, reports and information that suggests or shows traces of reporting bias will be removed and not be reported on the final publication. Multiple databases including Google scholar, SCOPUS and Web of Science, which use slightly different search strings and give out some papers which might not be available in the other databases. The resulting papers from these databases are all peer-reviewed and fact checked by published authors. A third party was consulted and asked to review our paper and check for imbalances during reporting. These eliminations of bias reporting will help avoid being bias when collecting and writing data on this subject. This makes the research more transparent so that future syntheses can more accurately assess both the promise and the limitations of IoT–cloud approaches in micronutrient monitoring. Results 3.1. Study selection Figure 6 illustrates the distribution of data sources utilised in this systematic review. The majority of the literature (50.00%) was retrieved from Google Scholar, reflecting its broad indexing scope and accessibility for multidisciplinary searches (Khabsa & Giles, 2014). Web of Science contributed 33.33% of the included studies, offering curated, high-quality peer-reviewed sources (Falagas et al., 2008), while SCOPUS accounted for 16.67%, providing comprehensive bibliographic coverage with advanced citation tracking capabilities (Burnham, 2006). This distribution underscores the importance of combining multiple databases to ensure both breadth and depth in literature retrieval, thereby minimising publication bias and enhancing the comprehensiveness of the review. 3.2. Study charecteristics Figure 7 presents the temporal distribution of published papers included in this review, illustrating trends in research activity over time. The earliest relevant studies date back to 2016, with gradual increases observed in subsequent years. A notable surge in publications appears from 2019 onwards, coinciding with the growing integration of IoT and cloud computing in environmental and agricultural monitoring (Miorandi et al., 2012; Atlam & Wills, 2020). Peaks in 2020 and 2023 indicate heightened scholarly interest, potentially driven by advancements in sensor technology, machine learning, and the increasing urgency of addressing micronutrient monitoring in both agricultural and public health contexts. This temporal trend underscores the field’s rapid evolution and the need for continuous synthesis of emerging evidence. Figure 8 depicts the distribution of publication types represented in the systematic review. The majority of included studies were peer-reviewed journal articles (72.22%), reflecting the dominance of formal, rigorously evaluated research in the field (Ware & Mabe, 2015). Conference proceedings accounted for 25.00%, indicating the active role of conferences in disseminating emerging findings and technical innovations before journal publication (Franceschet, 2010). A small proportion of studies (2.78%) originated from book chapters, which often provide conceptual or case study insights. This distribution suggests that while peer-reviewed journals remain the primary vehicle for scholarly communication on IoT–cloud integration for micronutrient monitoring, conferences play a significant role in the early visibility of novel approaches and prototypes. Figure 11 shows the distribution of water quality sensors employed in the studies, categorised by measurement type. Chemical parameter sensors dominated, comprising 43.90% of the total, with pH sensors, copper ion electrodes, and total dissolved solids meters among the most common. These instruments are essential for detecting nutrient levels, ion concentrations, and other chemical properties critical for environmental and agricultural water monitoring (Rode et al., 2016). Multiparameter sensors accounted for 17.54%, offering integrated monitoring of multiple water quality metrics within a single device, thus improving efficiency and reducing maintenance requirements (Banna et al., 2014). Physical parameter sensors, such as turbidity and water level sensors, made up 3.08%, primarily used for sediment load and hydrological assessments. A notable 35.03% of the studies did not specify the sensor type, indicating a significant gap in reporting standards that may affect reproducibility and meta-analytical synthesis. Figure 12 depicts the distribution of microcontroller and development boards used in the included studies. Arduino-based platforms dominated, representing 52.78% of usage, reflecting their affordability, ease of programming, and broad adoption in IoT prototyping for environmental monitoring (Banzi & Shiloh, 2014). Raspberry Pi systems accounted for 25.00%, offering greater computational capacity and suitability for data-intensive applications, including real-time analytics and machine learning integration (Halfacree & Upton, 2016). ESP32 devices contributed 13.89%, valued for their built-in Wi-Fi/Bluetooth capabilities, making them well-suited for wireless sensor networks. Other boards, such as the STM32F411RE (2.78%) and TelosB with CC2420 transceiver (2.78%), were less common but highlighted for their low-power operation in field deployments. Notably, 19.44% of studies did not specify the hardware platform, indicating a potential gap in reporting that limits reproducibility and technical comparability across studies. Figure 13 shows the distribution of communication technologies utilised in the studies, categorised by network type. Cellular communication technologies (all GSM variants) dominated with 46.43% of usage, reflecting their reliability and broad coverage in remote and rural monitoring contexts (Raza et al., 2017). IoT platforms and protocols accounted for 17.86%, enabling seamless device interconnection and cloud integration for real-time data transmission. Low-power wide-area networks (LPWAN), represented by LoRa, made up 17.86%, valued for long-range, energy-efficient communication suited to battery-powered field devices. Short-range wireless technologies such as Zigbee (7.14%) were applied in localised sensor networks, while wired/wireless LAN connections (3.57%) were rare, primarily in fixed-location monitoring setups. Notably, 7.14% of studies did not specify the communication method, suggesting a gap in methodological transparency that could affect replicability. Figure 14 presents the distribution of cloud computing platforms and integrations reported in the reviewed studies. Unspecified platforms formed the largest proportion (25.00%), indicating a considerable reporting gap that can hinder reproducibility and hinder platform-specific performance evaluation. Among identified services, AI-enhanced cloud solutions (AI-driven cloud computing for image processing, AI APIs, and cloud-based AI/ML) accounted for 14.29%, reflecting the growing role of artificial intelligence in automating data analysis and decision-making (Marjani et al., 2017). Cloud service providers such as Amazon Cloud and AWS IoT Core comprised 10.71%, supporting large-scale IoT deployments with robust data handling capabilities. Data storage services (cloud databases, cloud storage, SQL Server, and Firebase) represented 17.86%, highlighting the importance of secure, scalable repositories for high-frequency sensor data. IoT-focused cloud services (IoT-integrated cloud computing and WSN-cloud integrations) contributed 7.14%, demonstrating targeted architectures optimised for sensor-driven monitoring systems. This distribution indicates both a diversification of cloud adoption strategies and a need for more precise methodological documentation in future studies. Conclusion This systematic review demonstrates that integrating IoT and cloud computing technologies presents significant opportunities for enhancing micronutrient monitoring in environmental and agricultural systems. Quantitative analysis of the literature revealed strong research contributions from Asia, particularly India, with surface water being the most frequently monitored resource and chemical parameter sensors dominating measurement approaches. Arduino-based hardware and GSM communication technologies were the most common implementation choices, while a substantial proportion of studies failed to specify cloud platforms, indicating a gap in reporting standards. Despite promising advancements, challenges remain in addressing data volume management, energy efficiency, latency, interoperability, and security—particularly in remote and resource-limited settings. The growing integration of AI and edge–cloud architectures offers pathways for improving analytical capabilities, operational efficiency, and decision-making accuracy. Future research should prioritise developing robust, context-aware IoT–cloud frameworks, standardising reporting protocols, and conducting long-term field validations across diverse geographies. By closing these gaps, IoT–cloud solutions can play a central role in delivering scalable, cost-effective, and sustainable micronutrient monitoring systems, ultimately supporting improved agricultural productivity and public health outcomes. References Mahapatro PK, Panigrahi R, Padhy N (2024) Integrated Internet of Things and Artificial Intelligence System for Real-Time Multi-Nutrient Water Quality Analysis in Agriculture. Engineering Proceedings. ; 82(1):72. https://doi.org/10.3390/ecsa-11-20358 Garrido-Momparler V, Peris M (2022) Smart sensors in environmental/water quality monitoring using IoT and cloud services. Trends Environ Anal Chem 35:e00173. https://doi.org/10.1016/j.teac.2022.e00173 Wiryasaputra R, Huang C-Y, Lin Y-J, Yang C-T (2024) Sensors 24(4):1180. https://doi.org/10.3390/s24041180 . An IoT Real-Time Potable Water Quality Monitoring and Prediction Model Based on Cloud Computing Architecture Prapti DR, Shariff M, Man ARC, Ramli H, Perumal NM, T., Shariff M (2022) Internet of Things (IoT)-based aquaculture: An overview of IoT application on water quality monitoring. Reviews Aquaculture 14(2):979–992 Bhati A, Hiran KK, Vyas AK, Mijwil MM, Aljanabi M, Metwally ASM, Ahmad H (2024) Low cost artificial intelligence Internet of Things based water quality monitoring for rural areas. Internet Things 27:101255. https://doi.org/10.1016/j.iot.2024.101255 Alshami E, Ali M, Elsayed AEE, Eltoukhy, Zayed T (2024) IoT Innovations in Sustainable Water and Wastewater Management and Water Quality Monitoring: A Comprehensive Review of Advancements, Implications, and Future Directions, in IEEE Access, vol. 12, pp. 58427–58453. 10.1109/ACCESS.2024.3392573 Adebayo AI, Olubanjo KT, Fadeke AM, Uyanah JJ, Zirra AT, Igbaoreto WA, Fakoyede PD (2025) FROM STATIC SAMPLING TO DYNAMIC INSIGHTS: THE FUTURE OF WATER QUALITY MONITORING WITH SENSORS, IOT, AND DRONES. Sci World J 20(1):454–466 Sadeeq MM, Abdulkareem NM, Zeebaree SRM, Ahmed DM, Sami AS, Zebari RR (2021) IoT and Cloud Computing Issues, Challenges and Opportunities: A Review, Qubahan Academic Journal, vol. 1, no. 2, pp. 1–8, [Online]. Available: https://doi.org/10.48161/qaj.v1n2a36 Kadam D, Bhosale RS, Patil SS Multidisciplinary Model for Smart Agriculture using Internet-of-Things (IoT), International Journal of Innovative Research in Technology, vol. 12, no. 2, pp. 10–15, Feb. 2023. [Online]. Available: https://www.researchgate.net/publication/368574305 Botta A, De Donato W, Persico V, Pescapé A Integration of Cloud computing and Internet of Things: A survey, Future Generation Computer Systems, vol. 56, pp. 684–700, Mar. 2016. [Online]. Available: https://doi.org/10.1016/j.future.2016.11.031 Albahri SA et al (2019) A Survey on Internet of Things and Cloud Computing for Healthcare, Electronics, vol. 8, no. 7, p. 768, [Online]. Available: https://doi.org/10.3390/electronics8070768 Selvanarayanan R et al Empowering coffee farming using counterfactual recommendation based RNN driven IoT integrated soil quality command system, Scientific Reports , vol. 14, no. 1, p. 6269, Mar. 2024. [Online]. Available: https://www.nature.com/articles/s41598-024-56954-x Khanyi M, Xaba S, Mlotshwa N, Thango B, Matshaka L (2024) The Role of Data Networks and APIs in Enhancing Operational Efficiency in SME: A Systematic Review. Preprints 2024100848 Sofianopoulos S, Faka A, Chalkias C, SDI-Enabled Smart, Governance (2025) A Review (2015–2025) of IoT, AI and Geospatial Technologies—Applications and Challenges. Land 14(7):1399. https://doi.org/10.3390/land14071399 Xiao Y, Chen Y, Huang Y, Yan Y Surface Protection Technologies for Earthen Sites in the 21st Century: Hotspots, Evolution, and Future Trends in Digitalization, Intelligence, and Sustainability. Coatings 2025, 15(7), 855; https://doi.org/10.3390/coatings15070855 Skosana S, Mlambo S, Madiope T, Thango B (2024) Evaluating Wireless Network Technologies (3G, 4G, 5G) and Their Infrastructure: A Systematic Review. Preprints 2024101331 Yu W, Zhou X, Wang D, Dong J (2025) The Development and Construction of City Information Modeling (CIM): A Survey from Data Perspective. Appl Sci 15(9):4696. https://doi.org/10.3390/app15094696 Aldossary D, Aldahasi E, Balharith T, Helmy TA (2025) Systematic Literature Review on Load-Balancing Techniques in Fog Computing: Architectures, Strategies, and Emerging Trends. Computers 14(6):217. https://doi.org/10.3390/computers14060217 Jørgensen B, Ma Z (2025) Impact of EU Laws on the Adoption of AI and IoT in Advanced Building Energy Management Systems: A Review of Regulatory Barriers, Technological Challenges, and Economic Opportunities. Buildings 15(13):2160. https://doi.org/10.3390/buildings15132160 Mtjilibe T, Rameetse E, Mgwenya N, Thango Bonginkosi, Exploring the Challenges and Opportunities of Social Media for Organizational Engagement in SMEs: A Comprehensive Systematic Review (July 06, 2024). Available at SSRN: https://ssrn.com/abstract=4998542 or http://dx.doi.org/10.2139/ssrn.4998542 Mishra S, Shanmugam B, Yeo K, Thennadil S, SDN-Enabled IoT (2025) Secur Frameworks—A Rev Existing Challenges Technol 13(3):121. https://doi.org/10.3390/technologies13030121 Ahoa E, Kassahun A, Verdouw C, Tekinerdogan B Challenges and Solution Directions for the Integration of Smart Information Systems in the Agri-Food Sector. Sens 2025, 25(8), 2362; https://doi.org/10.3390/s25082362 Bankó M, Dyszewski S, Králová M, Limpek M, Papaioannou M, Choudhary G, Dragoni N (2025) Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges. Algorithms 18(4):209. https://doi.org/10.3390/a18040209 Thobejane LT, Thango BA (2024) Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review. Appl Sci 14:6097. https://doi.org/10.3390/app14146097 Fernando X, Lăzăroiu G (2024) Energy-Efficient Industrial Internet of Things in Green 6G Networks. Appl Sci 14(18):8558. https://doi.org/10.3390/app14188558 Andriulo F, Fiore M, Mongiello M, Traversa E, Zizzo V Edge Computing and Cloud Computing for Internet of Things: A Review. Inf 2024, 11(4), 71; https://doi.org/10.3390/informatics11040071 Danil L, Jahroh S, Syarief R, Taryana A Technological Innovation in Start-Ups on a Pathway to Achieving Sustainable Development Goal (SDG) 8: A Systematic Review. Sustain 2025, 17(3), 1220; https://doi.org/10.3390/su17031220 Marques P, Váz P, Silva J, Martins P, Abbasi M (2025) Real-Time Gesture-Based Hand Landmark Detection for Optimized Mobile Photo Capture and Synchronization. Electronics 14(4):704. https://doi.org/10.3390/electronics14040704 Thango BA, Obokoh L (2024) Techno-Economic Analysis of Hybrid Renewable Energy Systems for Power Interruptions. Syst Rev Eng 5(3):2108–2156. https://doi.org/10.3390/eng5030112 Paraskevas A, Madas M, Zeimpekis V, Fouskas K (2024) Smart Ports in Industry 4.0: A Systematic Literature Review. Logistics 8(1):28. https://doi.org/10.3390/logistics8010028 Cacciuttolo C, Guzmán V, Catriñir P, Atencio E (2024) Sensor Technologies for Safety Monitoring in Mine Tailings Storage Facilities: Solutions in the Industry 4.0 Era. Minerals 14(5):446. https://doi.org/10.3390/min14050446 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7335820","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":498157035,"identity":"65946ac2-c502-4cb1-8cd3-d910c1d13d93","order_by":0,"name":"M.K. Gule","email":"data:image/png;base64,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","orcid":"","institution":"University of Johannesburg","correspondingAuthor":true,"prefix":"","firstName":"M.K.","middleName":"","lastName":"Gule","suffix":""},{"id":498157036,"identity":"74f5168b-443c-41cc-99de-2c929cfb131c","order_by":1,"name":"R. Cossa","email":"","orcid":"","institution":"University of Johannesburg","correspondingAuthor":false,"prefix":"","firstName":"R.","middleName":"","lastName":"Cossa","suffix":""},{"id":498157037,"identity":"d77ece6b-44e6-4100-8e9d-a580717b3277","order_by":2,"name":"P. Khowa","email":"","orcid":"","institution":"University of Johannesburg","correspondingAuthor":false,"prefix":"","firstName":"P.","middleName":"","lastName":"Khowa","suffix":""}],"badges":[],"createdAt":"2025-08-09 20:30:49","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7335820/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7335820/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88883665,"identity":"84d09d7a-b614-47d9-9929-58aea9a4bfc5","added_by":"auto","created_at":"2025-08-12 11:37:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":286213,"visible":true,"origin":"","legend":"\u003cp\u003eProcedures and Stages of the Review.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7335820/v1/60155d879bc5de79eda1e041.png"},{"id":88883666,"identity":"70b3f5f8-e14b-43fa-bfa8-7040ac9fa9f9","added_by":"auto","created_at":"2025-08-12 11:37:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32002,"visible":true,"origin":"","legend":"\u003cp\u003eData collection process flowchart.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7335820/v1/b7bf69a7939ff6ba80fa2959.png"},{"id":88884818,"identity":"d94ebc32-69f8-45d6-89af-a551682e256f","added_by":"auto","created_at":"2025-08-12 11:45:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49918,"visible":true,"origin":"","legend":"\u003cp\u003eData selection and extraction.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7335820/v1/bb68449fc0132571d5379cad.png"},{"id":88884830,"identity":"a3287c60-cbf9-401c-9563-5365622fc45c","added_by":"auto","created_at":"2025-08-12 11:45:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":90396,"visible":true,"origin":"","legend":"\u003cp\u003eRisk of Bias Assessment Process\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7335820/v1/0e3997661ffec29671f1510b.png"},{"id":88883668,"identity":"05e3752a-e983-4379-b242-e8bd067d89a1","added_by":"auto","created_at":"2025-08-12 11:37:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":490524,"visible":true,"origin":"","legend":"\u003cp\u003eSystematic Review Process for Issues, Challenges, and Opportunities in Micronutrient Monitoring.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7335820/v1/97220b87ca8704ac70721518.png"},{"id":88883670,"identity":"60007859-936c-4d0c-903c-e190ad6365a0","added_by":"auto","created_at":"2025-08-12 11:37:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":103848,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of data sources used in the systematic review.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7335820/v1/11d7163a6a05bb2b6050665f.png"},{"id":88886552,"identity":"be68a27d-e171-4fff-aec3-832b315225f9","added_by":"auto","created_at":"2025-08-12 12:01:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":233234,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Distribution by Published Papers.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7335820/v1/bb67324eceedb45a19d8035a.png"},{"id":88883672,"identity":"36ded0ff-4ff7-4873-81ef-ff615bc422c1","added_by":"auto","created_at":"2025-08-12 11:37:04","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":103463,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of publication types included in the systematic review.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7335820/v1/10c5058413f7781f8e5f09e5.png"},{"id":88883675,"identity":"29af60ed-b025-4f4b-9fd3-eaa6f42f7092","added_by":"auto","created_at":"2025-08-12 11:37:04","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":365142,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic distribution of studies included in the systematic review.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7335820/v1/d3b0e25a62c0a12ece8a95da.png"},{"id":88883678,"identity":"cff87bc3-da10-4878-9fe5-0965fc0157e7","added_by":"auto","created_at":"2025-08-12 11:37:04","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":304456,"visible":true,"origin":"","legend":"\u003cp\u003eTypes of water sources monitored in the studies included in the systematic review.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7335820/v1/5e8a37e2d9b34d804b89a713.png"},{"id":88887756,"identity":"2a74a207-9185-4cc5-a615-416f1e17b151","added_by":"auto","created_at":"2025-08-12 12:09:04","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":339893,"visible":true,"origin":"","legend":"\u003cp\u003eWater quality sensors used in the studies included in the systematic review.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-7335820/v1/f04c14495d692b09b8c55e69.png"},{"id":88885471,"identity":"c29e1282-ec0a-4d44-8507-653bf1277d91","added_by":"auto","created_at":"2025-08-12 11:53:04","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":260376,"visible":true,"origin":"","legend":"\u003cp\u003eMicrocontroller and development boards used in the studies included in the systematic review.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-7335820/v1/c77cacefdb91cff5e9a3a5a2.png"},{"id":88884826,"identity":"4a4f67e9-7b81-441d-902d-ef2e7611faef","added_by":"auto","created_at":"2025-08-12 11:45:04","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":190024,"visible":true,"origin":"","legend":"\u003cp\u003eCommunication technologies used in the studies included in the systematic review.\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-7335820/v1/ab4f25fc65719807c224a36d.png"},{"id":88885467,"identity":"61c9d746-daab-4f50-ad9d-2d296ea15325","added_by":"auto","created_at":"2025-08-12 11:53:04","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":381263,"visible":true,"origin":"","legend":"\u003cp\u003eCloud computing platforms and integrations used in the studies included in the systematic review.\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-7335820/v1/9a557461c90dfd07a0231265.png"},{"id":89063874,"identity":"2e946e6c-3a9f-4079-8c30-d5e0904ada16","added_by":"auto","created_at":"2025-08-14 10:09:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3565362,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7335820/v1/6f43a921-b697-4bdd-be7c-53111ed761e5.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eTechnological Integration for Micronutrient Monitoring in Water Systems\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eMicronutrients are a very important substance and are needed for humans to stay healthy. They basically vitamins and minerals that are found in small amounts and are needed by the body to produce energy and to do regular bodily functions like boost the immune system, help produce blood or maintain bone health (WHO, 2022). Micronutrient deficiency is one of the major problems in the world, were hundreds of millions of people suffer from this. Micronutrient deficiency is defined as a lack of essential vitamins and minerals that are required in small amounts by the body for proper growth and development (WHO, 2022). To the people with lack of micronutrients, this could lead to serious health problems, affecting not only physical but mental health too. But micronutrients are not just found in the human body or essential for the human body only, they are also found in foods and the soil, such as copper, manganese, chlorine, boron and zinc to name a few (D. KUMAR, 2022). They are also crucial for a balanced health crop growth, like flowering, root development and fruit production. Even though they are found in very small amounts, if the soil is micronutrient deficient it could lead to decreased crop yield and overall plant productivity (D. KUMAR, 2022). Traditional ways of monitoring micronutrients are not economical and are delayed, even if they were accurate (M. BELGIU, 2023). They did not offer remote sensing and real-time monitoring systems, which is not ideal especially in this modern-day age of advanced technology, where a lot of things are going digital. All of the issue mentioned above are the reasons why there is a lot of research and interests in micronutrients monitoring systems. Theres an abundance of new advanced technologies that could be used and/or integrated with the traditional methods of tracking micronutrients to make better systems for monitoring micronutrients. Some the ways can be of integration with (IoTs) Internet of Things, sensors and cloud computing (S. AHMED, 2023).\u003c/p\u003e\u003cp\u003eThe IoT are basically a include network of systems embedded with technology such as sensors and software and can include mechanical and digital machines and consumer objects (S. AHMED, 2023). This technology can help us achieve better monitoring of nutrients in real time using IoTs and its sensors to detect micronutrients and send real time monitored data to our device remotely to show use the results. They usually use strong sensitive sensors that can even detect the slightest change in micronutrients, as micronutrients are normally found in very small quantities in the soil (A. ANJALI, 2024). The data recorded by the sensors will then be stored, analysed and interpreted by the cloud-computing section of the system, via processing and machine learning techniques, the same data will also be shown on the display of a digital device, for users to access anytime. The IoT and cloud computers detect, upload, analyse, and present micronutrient metrics automatically with minimal human intervention (A. ANJALI, 2024).\u003c/p\u003e\u003cp\u003eThe proposed system is very good idea, but in certain instances it is not favourable. Some of the issues faced with these systems are network/connection problems, integration with other systems/devices and sensor failures (S. AHMED, 2023). Network connectivity, especially in rural areas or in times or extreme environments conditions like heavy rain and strong winds, tends to be weak, or in times of power outages like load shedding, wireless networks are disrupted. There are also cloud storage and security problems. Cloud storage can get expensive if large amounts of data need to be stored, also on the issue of security and privacy concerns, where the system can be hacked and user data that is sensitive can be stolen (Staff, 2025). Another issue that can arise is the stubbornness of other people who might not be willing to change the traditional ways of doing things that they are used to, as they are comfortable with familiarity.\u003c/p\u003e\u003cp\u003eThe are a lot of opportunities for future growth of these systems as we make breakthroughs in technology advancements in the world. We can use the real time monitoring capabilities of the system to monitor and check health of people and not just for soil and plant applications. This will help people to manage and take care of their personal health easier, by providing personalized nutrition management, prevent diet-related health problems and remind patients to take their medicine in time for example. The real time monitoring will also help improve and promote sustainable agricultural practices in the industries of crop production. Integration with AI and machine learning will help enhance the systems automatic functioning capabilities, allowing for more accurate and precise readings of micronutrients (Umar Farouk Mustapha, 2021). Lastly the use of cloud can be made more secure by building firewalls and better access credential login details of customers to prevent privacy concerns.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative Analysis of the Existing Review Works and Proposed Systematic Review on IoT and Cloud Computing Issues, Challenges, and Opportunities in Micronutrient Monitoring.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCites\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eContribution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePros\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBhati, A.2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExplores integration of IoT with Cloud Computing, highlights challenges (security, data management, scalability)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnables efficient data storage and processing for IoT and Supports scalability for IIoT data.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInconclusive results on integration and lacks concrete implementation models\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlshami, E.2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProposes a real-time multi-nutrient water quality monitoring system using IoT sensors to predict nutrient levels like N, P, K, pH, BOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAccurate predictions (90% accuracy), Real-time monitoring, Combines AI\u0026thinsp;+\u0026thinsp;IoT for efficiency \u0026amp; Improves decision-making in precision agriculture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLimited data availability may affect model training; Environmental factors can reduce sensor accuracy \u0026amp; Ensemble models may be computationally intensive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdebayo, A.2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eComprehensive review of smart sensors used for continuous in-line environmental and water quality monitoring using IoT and cloud services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnables real-time, autonomous monitoring, Integrates wireless communication and cloud storage\u003c/p\u003e\u003cp\u003e, Reduces cost of frequent lab-based testing.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEnergy consumption challenges, High initial cost of deployment, Reliability and useful life of wireless systems need improvement.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM. M. Sadeeq,2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDeveloped an IoT-based water quality monitoring system using NB-IoT and Arduino, integrated with machine learning models (DT, RF, SVM, NN, GB).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReal-time monitoring via mobile/web, Low-cost and self-assembled sensors and SMS alerts via LINE.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eData limited to Tunghai campus, Limited scope of contaminants (no E. coli, chlorine, HABs yet).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD. Kadam,2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConducted an extensive review (2011\u0026ndash;2020) of IoT-based water quality monitoring in aquaculture. Categorized research by environment, approach, parameters, and solutions.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePrioritizes key water parameters (temp, DO, pH), Supports Aquaculture 4.0 development\u0026amp; Encourages use of smart sensors and automation.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLimited focus on marine aquaculture (19%) and Emphasizes need for long-term practical research.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA. Botta,2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProposed a novel, low-cost, low-power IoT-based water quality monitoring system for traditional distribution networks using sensors and machine learning for real-time analysis.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReal-time water quality prediction via ML, Low-cost, solar-powered, low-energy sensors and Effective in rural areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePerformance dependent on network/cloud availability\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS. A. Albahri et al,2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConducted a systematic review on IoT innovations in water and wastewater management, focusing on wireless technologies, sensor integration, and communication protocols.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReal-time monitoring; use of multiple sensors (pH, flow, level); enhanced efficiency with Sigfox, Zigbee, MQTT; improved decision-making; automation and scalability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChallenges include sensor accuracy, energy optimization, communication reliability, limited interdisciplinary collaboration\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR. Selvanaran,2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePresented a comprehensive review on the use of sensors, IoT, and drones for real-time, adaptive water quality monitoring.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnables real-time, high-resolution data acquisition, Uses AI and cloud for predictive modelling and Drones improve accessibility and coverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh implementation and maintenance costs, Cybersecurity vulnerabilities and Lack of standardization for integration.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWe also identify, in our systematic review, some of the most egregious research gaps on the topic of applications of Internet of Things (IoT) and cloud computing technology to micronutrient monitoring. They not only tell us where already there is knowledge gap but also provide useful pointers to guide future research so that such technologies are realistically better achieved and scaled. Its individual application requirements of sensitivity, timely aggregating the data, and ambient condition versatility of were installed give way, too often, to more vague appeals. Even some papers focus more on situating sensor functions and technical specifics rather than discussing the implementation complications that micronutrient deficiency occurs at most at the rural or lower-income areas. Yet interesting as both the technologies are as monitors of right monitoring, plans are more sketched independently of each other and not together collectively as an effort to consider both their implications on rightful data, action as much as policy, and final nutritional impact of a project. In addition, existing literature to date relies on either pilot data or test-controlled environments, which constrain the degree to which scalability over the longer term, sus-stainability across systems, and adoption in the field can be achieved. There is still a pressing need for longitudinal and field research into the longer-term effect of these technologies across various geographies and socio-economic contexts.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Research questions\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAlthough IoT and cloud computing are increasingly applied in healthcare and agriculture, their use in micronutrient monitoring is still limited. Most studies focus on the individual technologies or general nutrient monitoring, without assessing how IoT\u0026ndash;cloud integration affects the validity, usability, and responsiveness of micronutrient data. This review addresses the following research questions:\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eWhy do recent studies emphasise integration aspects such as data validity, user accessibility, and latency, rather than cloud infrastructure and sensor deployment?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHow can IoT-based monitoring systems be scaled to provide affordable, precise, real-time micronutrient information for vulnerable populations?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eWhat are the current applications of cloud computing for storage optimisation, analysis, and decision-making in micronutrient programmes?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHow are AI-enabled analytics and smart alert functionalities being incorporated into micronutrient monitoring systems?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHow are IoT\u0026ndash;cloud systems adapted to different geographic and socio-economic contexts?\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Hypotheses development\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe review is based on the hypothesis that integrating IoT and cloud computing improves micronutrient monitoring by enabling accurate, real-time measurements and data processing, despite connectivity and security challenges. The following hypotheses are examined:\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eH1: Methodological inconsistencies, lack of standardised reporting frameworks, and varied study designs contribute to underreporting in micronutrient research.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eH2: IoT\u0026ndash;cloud integration enables accurate, real-time micronutrient monitoring.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eH3: Combining IoT\u0026ndash;cloud systems with machine learning improves monitoring efficiency and analysis.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eH4: Cloud-based business intelligence models support micronutrient monitoring by enabling real-time data handling and analysis in health and agriculture.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Rationale\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis review evaluates the use of IoT and cloud computing in micronutrient monitoring, focusing on data collection, processing, and storage. These systems have been applied in varied geographic and economic contexts, with adoption influenced by infrastructure and technical capacity. Challenges include system integration, connectivity, and data privacy. The review examines literature from 2014\u0026ndash;2025 to summarise how IoT\u0026ndash;cloud approaches are used in agricultural and health-related micronutrient monitoring.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e1.4 Objectives\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe objectives are to:\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eDocument current applications of IoT and cloud computing in micronutrient monitoring.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIdentify technical and operational challenges to adoption.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDescribe the technologies and architectures currently used.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSummarise the main approaches reported in the literature between 2014 and 2025.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e1.5 Research contributions\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis review:\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eIdentifies IoT\u0026ndash;cloud approaches used for real-time micronutrient monitoring in water systems.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSummarises technical and infrastructural challenges affecting system adoption and scale-up.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eOutlines areas where AI, edge\u0026ndash;cloud systems, and low-power sensing are already being applied in nutrient monitoring.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHighlights gaps in the literature, including underreporting of technical details such as sensor types and cloud platforms.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e1.6 Research novelty\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTo date, no systematic review has specifically examined IoT\u0026ndash;cloud integration for micronutrient monitoring. Existing studies generally focus on broader agricultural or healthcare contexts. This review is novel in its focus on micronutrient-specific applications, providing:\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eAn analysis of technical, operational, and contextual issues in adopting IoT\u0026ndash;cloud monitoring systems for micronutrients.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eA synthesis of reported technologies and system architectures used between 2014 and 2025.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis systematic review follows the guidelines of the Preferred Reporting Item for Systematic Reviews and Meta Analysis (PRISMA). In this subsection, the study outlines the methodology employed to conduct a systematic review focusing on the issues, challenges and opportunities in IoT and cloud computing integrated systems in micronutrients monitoring. The study is based on a review of literature published over the last decade, from 2014 to 2025. The review focuses on peer reviewed studies found from online database like Google Scholar, Web of Science and SCOPUS. The studies are searched used a search string tailored specifically to the type of online database. These resources are chosen because of their reliability, credibility and reputation in the research field.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Eligibility criteria\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eA systematic study of all peer-reviewed and published research works that focuses on the integration of IoT and cloud computing in micronutrients monitoring systems. Only research works published in English between 2014 and 2025 were included in the analysis. A proper criterion for inclusion was adapted to ensure the inclusion of research papers that specifically focus on studies that concern and address the challenges, issues and opportunities related to the application of IoT and cloud computing integrated systems, that monitor micronutrient levels in water, soil, agriculture and healthcare. The inclusion and exclusion criteria for this study are tabulated as in Table\u0026nbsp;2 (Khanyi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sofianopoulos et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xiao et al., 2025; Skosana et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Aldossary et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; J\u0026oslash;rgensen \u0026amp; Ma, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mtjilibe et al., 2024; Mishra et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ahoa et al., 2025; Bank\u0026oacute; et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Thobejane \u0026amp; Thango, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fernando \u0026amp; Lăzăroiu, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Andriulo et al., 2024; Danil et al., 2025; Marques et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Thango \u0026amp; Obokoh, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Paraskevas et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cacciuttolo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e. \u003cb\u003eTable\u0026nbsp;2.\u003c/b\u003e Proposed Inclusion and Exclusion Criteria.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCriteria\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInclusion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExclusion\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArticle papers focusing on issues, challenges or opportunities of IoT and cloud computing systems in micronutrients monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eArticle that do not focus on addressing micronutrient monitoring nor focus on IoT and cloud computing platforms/systems\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResearch Framework\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe Articles must include research framework or methodology or methodology for applications related to IoT and cloud computing systems in micronutrients monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eArticles must exclude research framework or methodology that are not related IoT and cloud computing systems in micronutrients monitoring\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLanguage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMust be written in English\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eArticles published in languages other than English\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeriod\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArticles between 2014 to 2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eArticles outside 2014 and 2025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Information sources\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe data for this review was gathered from a range of peer-reviewed journal articles, conference proceedings, and technical papers published between 2014 and 2025. Primary sources included established academic databases such as SCOPUS, Web of Science, and Google Scholar, ensuring high academic quality and relevance. The selected studies focused on the intersection of Internet of Things (IoT) and Cloud Computing in the context of micronutrient monitoring and water quality analysis. Table\u0026nbsp;2 outlines the databases consulted and highlights their relevance to the review\u0026rsquo;s scope and inclusion criteria (Khanyi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sofianopoulos et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xiao et al., 2025; Skosana et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Aldossary et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; J\u0026oslash;rgensen \u0026amp; Ma, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mtjilibe et al., 2024; Mishra et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ahoa et al., 2025; Bank\u0026oacute; et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Thobejane \u0026amp; Thango, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fernando \u0026amp; Lăzăroiu, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Andriulo et al., 2024; Danil et al., 2025; Marques et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Thango \u0026amp; Obokoh, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Paraskevas et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cacciuttolo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Search strategies\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe data in sheer spirit of this review, literature has been searched in high-impact research bibliographic scholarly databases with a predisposition towards research combining IoT and cloud computing and its adoption in micronutrient monitoring in medicine and agriculture. Every effort has been made to adopt technological topics and technologies as and when possible, in applications like system design, data handling, and deployment-related issues in field deployment.\u003c/p\u003e\u003cp\u003eSystematic searching of the first three repositories, Google Scholar, Scopus, and Web of Science, was conducted. A search keyword was given and used to search for:\u003c/p\u003e\u003cp\u003e(((((((\"Internet of Things\" OR \"IoT\") AND (\"Cloud Computing\") AND (\"Micronutrient Monitoring\" OR \"Micronutrient Tracking\") AND (\"Agriculture\" OR \"Public Health\") AND (\"Real-time Data\" OR \"Data Management\" OR \"Remote Sensing\"))\u003c/p\u003e\u003cp\u003eThese words were employed in a search effort for research on IoT and cloud technology adoption for micronutrient status monitoring, especially in low-resource or rural environments.\u003c/p\u003e\u003cp\u003eLiterature review was limited to 2014\u0026ndash;2025 to cite current technological development. The initial search in Google Scholar produced 35 articles, 33 in Scopus, and 25 in Web of Science. All articles were screened for relevance, and research articles only that were pertinent to the purpose of the study were synthesized in final analysis.\u003c/p\u003e\u003cp\u003eThis stage offered a solid and shared platform to deliberate on the research problem and viability of applying IoT and cloud computing in tracking micronutrients. See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e below.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults Achieved from Literature Search.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOnline Repository\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of results\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGoogle Scholar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeb of Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScopus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Selection process\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe screening and selection of relevant literature followed a structured and systematic approach to ensure both the quality and relevance of the studies included in this review. Articles were initially retrieved from three reputable databases: SCOPUS, Web of Science, and Google Scholar. All records were exported into Microsoft Excel for proper organization, removal of duplicate entries, and tracking of inclusion/exclusion decisions. The inclusion criteria specified that articles must explicitly address IoT and cloud computing technologies in the context of micronutrient monitoring, water quality analysis, or micronutrient contamination detection. Eligible studies were required to be published between 2015 and 2025, be written in English, and include a clear methodology involving the application or evaluation of IoT-cloud systems in micronutrient or environmental monitoring. Title and Abstract Screening: Each article\u0026rsquo;s title and abstract were independently reviewed to preliminarily assess relevance to the review objectives. Full-Text Screening: Studies that met the initial screening criteria were further subjected to a comprehensive full-text review. Each article was evaluated by at least two independent reviewers, and disagreements were resolved by the third reviewer through consensus (Khanyi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sofianopoulos et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xiao et al., 2025; Skosana et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Aldossary et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; J\u0026oslash;rgensen \u0026amp; Ma, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mtjilibe et al., 2024; Mishra et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ahoa et al., 2025; Bank\u0026oacute; et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Thobejane \u0026amp; Thango, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fernando \u0026amp; Lăzăroiu, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Andriulo et al., 2024; Danil et al., 2025; Marques et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Thango \u0026amp; Obokoh, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Paraskevas et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cacciuttolo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Data collection process\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe data collection process was conducted using a standardized approach to ensure accuracy, consistency, and transparency (Khanyi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sofianopoulos et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xiao et al., 2025; Skosana et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Aldossary et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; J\u0026oslash;rgensen \u0026amp; Ma, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mtjilibe et al., 2024; Mishra et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ahoa et al., 2025; Bank\u0026oacute; et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Thobejane \u0026amp; Thango, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fernando \u0026amp; Lăzăroiu, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Andriulo et al., 2024; Danil et al., 2025; Marques et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Thango \u0026amp; Obokoh, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Paraskevas et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cacciuttolo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A structured data extraction form was developed in Microsoft Excel to capture all relevant information, including study title, year, author(s), study objectives, methodology, IoT and cloud computing technologies used, types of micronutrients monitored, key findings, and limitations. Each included article was reviewed by two independent reviewers who extracted data separately to reduce the risk of bias or errors. Any discrepancies between the two reviewers were resolved through discussion, and if consensus could not be reached, a third reviewer acted as an arbitrator. No automation tools were used in the data extraction phase; all information was collected manually to preserve context and ensure a comprehensive understanding of each study. Where key data were missing or unclear, attempts were made to contact the original authors via email to request clarification or supplementary information. This process ensured the completeness and accuracy of the dataset used in the synthesis and analysis stages of the review.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Data items\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis section provides a comprehensive overview of the data items sought in this systematic review, focusing on searching papers across different database and analysing the data to look for specific outcomes, related to the use of IoT and cloud computing devices for micronutrient monitoring.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e2.6.1 Data Collection Method\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe data collection methods include different parts of an integrated system designed to find a specific outcome that measure and indicate micronutrient content in a substance. The IoT device will use its sensors to detect micronutrient contents. It will also offer real time analysis of user health in devices like mobile phones or wearables smart watches, which will give the user information about their dietary needs, using data specifically catered to them like health goals, diet needs and restrictions. The cloud data processor stores and analyses the data from sensors to make sure the recommendations given to the user matches their needs and indicates inconsistencies. Cloud based platforms like AWS IoT or Azure IoT will be used for security measures, which will make sure of secured data storage and secured transmission of data from one database to another, with real-time monitoring capabilities. Software for Edge or Cloud processing will be used to make sure that correct and clean data is processed and produced. Hybrid integration of IoT devices with cloud computers will be and integration of manual user input with automated sensor data processing.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e2.6.2. Definition of collected data variables\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis review identified and extracted several critical variables from the included studies to evaluate the technological, operational, and contextual dimensions of IoT and cloud computing systems applied in micronutrient monitoring, especially in water quality environments. These variables were selected based on their direct relevance to deployment characteristics, data communication strategies, challenges, and innovation potential in the field.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eData Variables Collected.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eField\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudy characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeployment region, monitoring environment, sample size, IoT/cloud architecture, and communication protocol.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntervention\u003c/p\u003e\u003cp\u003echaracteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncludes key performance metrics (sensor accuracy, data latency, uptime, energy use).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEconomic factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncludes the investment valuation of IoT/cloud deployments, the competitive advantages gained, and the overall ROI.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExternal influences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncludes the vendor ecosystem, adoption and demand trends, analytics-algorithm updates, emerging technological innovations, and environment‐specific monitoring drivers.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.7. Study risk of bias assessment\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eRisk of bias was assessed independently by four reviewers to prevent bias. Disagreements' resolutions were achieved through discussion, and any persisting disagreements were referred to a fourth reviewer to make the final decision. Where research appeared not to have taken tidily well-constructed methodological data with it\u0026mdash;where, say, off-the-shelf cloud platforms or IoT devices had been employed a secondary cross-matching follow-up search in Web of Science, Scopus, and Google Scholar was thus performed. Additional manual online repository searching was also carried out in the mindset of not wanting to be in any way biased so that one obtains the best that there is and most comprehensive review. No automated procedure has been used here.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e2.8. Synthesis methods\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe following flowchart illustrates the scientific methodology applied in our literature review on the use of cloud computing and IoT applications to monitor micronutrients. It begins with Study Selection, where concerned studies are searched and screened against previously defined a priori eligibility criteria. It then goes to Data Standardization in pre-processing data by cleaning data and data transformation in an attempt to make data homogenous from heterogenous sources. In Data Analysis, data is presented in tables and figures and primary analysis is done. Then the flow moves to Heterogeneity Assessment, where we evaluate heterogeneities between studies by sensitivity analysis and subgroup. Finally, Bias Assessment is done to monitor possible biases and maintain transparency of review. Following this sequential flow makes sure proper and accurate integration of the research that dominates the field is done.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn this cloud computing- and IoT-based micronutrient monitoring systematic review, we employed strict synthesis protocols where our results will be transparent, valid, and reproducible. In deciding whether to include or exclude a study for synthesis, we properly documented all study data and checked it against our pre-specified inclusion criteria. This was done in an attempt to include within the review the most valid and also the most methodologically sound studies Missing or missing data were addressed through imputation methods and periodic data cleaning to pre-specified comparability ahead of time among studies. Data were quantified through tabular summary and plots to enable us to publish effect estimates and confidence intervals without restriction and identify trends or outliers. Random-effects meta-analysis model was used to pool after monitoring practice, technology, and settings adjustment. Subgroup analysis was also performed to examine the effect of geography, extent of monitoring coverage, and whether the system is IoT- or cloud-based in nature. Subgroup and meta-regression analysis enabled us to control for the likely causes of heterogeneity and again to further clarify the degree to which each contextual parameter is driving the performance of IoT and cloud-based systems for micronutrient monitoring.\u003c/p\u003e\u003cp\u003eFor the purposes of maintaining reliability stability to our results, sensitivity analysis was carried out to pre-estimate half study exclusion or alteration of the analysis models on our results. Systematic method enabled us to develop evidence-based results that would guide policy-makers, practitioners, and researchers to an effort for improvement in nutrition ssurveillance through specific new technological improvement.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e2.8.1. Eligibility for Synthesis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTo determine eligibility for inclusion in the synthesis, each study was systematically assessed based on its alignment with the core themes of the review: the integration of IoT technologies, the application of cloud computing, and their combined role in micronutrient monitoring or water quality assessment. The eligibility process involved extracting key study characteristics such as the specific micronutrient or water quality parameter monitored (nitrate, calcium, iron), the type and architecture of IoT and cloud systems employed, the monitoring environment (such as agricultural fields or aquatic ecosystems), and the documented issues, challenges, or opportunities identified in the study. Studies were grouped into thematic categories that reflected their primary contributions such as technical limitations, deployment strategies, data accuracy, or scalability solutions to ensure a coherent synthesis. Only studies that presented original findings, a clear methodology, and direct relevance to the intersection of IoT, cloud computing, and micronutrient monitoring were retained for the final synthesis.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e2.8.2. Data Preparation for Synthesis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn this review, the methods used involved converting or standardizing data collected from various studies to ensure consistency before synthesis. For example, when effect sizes were reported differently across studies, algebraic manipulations were employed to convert these into a uniform scale, such as converting odds ratios to risk ratios where appropriate. Additionally, handling missing data was a critical aspect of the analysis. Missing summary statistics, such as standard deviations or effect sizes, were imputed using established statistical methods like multiple imputation. This approach ensured that the dataset was comprehensive and robust, allowing for a more accurate and reliable analysis (Khanyi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sofianopoulos et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xiao et al., 2025; Skosana et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Aldossary et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; J\u0026oslash;rgensen \u0026amp; Ma, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mtjilibe et al., 2024; Mishra et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ahoa et al., 2025; Bank\u0026oacute; et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Thobejane \u0026amp; Thango, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fernando \u0026amp; Lăzăroiu, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Andriulo et al., 2024; Danil et al., 2025; Marques et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Thango \u0026amp; Obokoh, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Paraskevas et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cacciuttolo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e2.8.3. Tabulation and Visual Display of Results\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSynthesize the results from the selected studies, a narrative synthesis approach was employed, which involved categorizing the studies based on common themes and outcomes. This approach allowed for a comprehensive comparison of the various IoT and cloud computing frameworks used in micronutrient monitoring (Khanyi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sofianopoulos et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xiao et al., 2025; Skosana et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Aldossary et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; J\u0026oslash;rgensen \u0026amp; Ma, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mtjilibe et al., 2024; Mishra et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ahoa et al., 2025; Bank\u0026oacute; et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Thobejane \u0026amp; Thango, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fernando \u0026amp; Lăzăroiu, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Andriulo et al., 2024; Danil et al., 2025; Marques et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Thango \u0026amp; Obokoh, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Paraskevas et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cacciuttolo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The synthesis was structured around the major findings, focusing on the technological challenges, solutions, and opportunities identified across studies. For studies that included quantitative data, the synthesis utilized descriptive statistics to summarize key metrics such as accuracy, efficiency, and scalability of IoT systems in micronutrient detection. In cases where meta-analysis was not feasible due to heterogeneity in methodologies or outcomes, the findings were aggregated narratively, emphasizing common trends and discrepancies. The synthesis was further informed by identifying patterns in sensor types, cloud integration methods, and the impact of various environmental factors on the monitoring systems. Where applicable, the synthesis included a comparative analysis of the effectiveness of different approaches in specific use cases, highlighting strengths and weaknesses across different sensor technologies and IoT architectures.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e2.8.4. Synthesis of Results\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eDuring our manual search on online repositories such as Google Scholar, Scopus, and Web of Science, we carefully reviewed and synthesized the results of relevant studies. The approach to data synthesis was guided by the nature of the data and the degree of variability observed across studies. Based on the findings from our search, we manually assessed the applicability of both fixed-effects and random-effects models, depending on the level of heterogeneity among study results. The selection of the model was determined by the characteristics of the data and our assumptions about the consistency of effects across studies. After exporting the data to Excel, we created charts to visually inspect the data, allowing us to identify patterns of variability and potential heterogeneity across the studies. This initial visual inspection provided an overview of how study results differed from one another, facilitating a more nuanced analysis.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e2.8.5. Exploring Causes of Heterogeneity\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTo analyse heterogeneity explanations across studies for cloud computing and IoT in micronutrient monitoring, subgroup analysis and meta-regression were employed. These included heterogeneity in setting, intervention as type of IoT or cloud computing utilized, monitoring systems, and geographic or environmental settings. Controlled were variables such as the nature of the nutrient being measured, scale of deployment (national or community), and local infrastructure capability in the effort to quantify their impact on successful deployment as well as data reliability. It aimed to establish patterns and indeed correlations of outcome variation towards enhancing our understanding of how these technologies under varying conditions.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\u003ch2\u003e2.8.6 Synthesis methods\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe strength of the findings of the syntheses under different assumptions and choices of methodology carried out in the review was analysed using sensitivity analyses. These covered testing the removal of high-risk bias studies, as well as the application of alternative statistical models, to minimize the over-effect of specific studies or analytical preferences on conclusions drawn. This approach helped prove the validity and reliability of findings by rendering known sources of bias operational and replicability of outcomes across different settings of analysis.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e2.9. Synthesis methods\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn the course of conducting our systematic review of IoT and cloud computing technology integration and impact on micronutrient monitoring, it was essential that we set the risk of bias for missing data or selectively reported data. Reporting biases like selective outcomes reporting and selective publication have very significant implications to the completeness and validity of our synthesis. It is owing to this that we applied a tight and methodologically conscientious strategy in managing these threats.\u003c/p\u003e\u003cp\u003eTo identify and correct likely bias, we used traditional statistical as well as graph-based techniques. Specifically, contour-enhanced funnel plots were used as a graph diagnostic statistic for asymmetry tests to investigate publication bias. The plots provided visual proof of whether missing studies would be the result of random variation or reflective of systematic bias, where IoT deployment or cloud interventions have been underreported because of non-significant or inconclusive findings.\u003c/p\u003e\u003cp\u003eAlong with developing new tools, we emphasized utilizing established, literature-supported techniques. Adding contours to funnel plots was an effective way of showing study distribution and accuracy so that our synthesis could bridge gaps and inconsistencies between data sources. Subjectivity bias was also minimized with effort directed towards duplicate assessment by multiple reviewers. There were also discrepancies in assessment, which were agreed and resolved either by consensus or later guidance from an expert in systematic review methodology. This joint exercise enhanced the independence and strength of our study.\u003c/p\u003e\u003cp\u003eWe deliberately avoided using bias estimation reporting automation tooling for this review. A human approach\u0026mdash;typing information into software like Excel for data visualization and plotting\u0026mdash;offered an intimate review of the dataset. The human approach allowed us to spot fine trends and fine whispers of bias, particularly in the niche field of micronutrient tracking with IoT and cloud networks.\u003c/p\u003e\u003cp\u003eTo further enhance the comprehensiveness of our synthesis, hand searches were performed across a range of academic databases including Google Scholar, Scopus, and Web of Science. Through source triangulation, thorough cross-referencing of the studies and allowing validation for consistency was facilitated in our extraction process and interpretation.\u003c/p\u003e\u003cp\u003eBased on the peculiar topology of cloud computing and IoT research, especially nutritional health monitoring, we adapted conventional bias measures to suit the peculiarity of such research. Outputs in such research are also presented differently from those in social science or clinical research, and methodology modifications were required for context validity. The adaptation to suit our needs made our synthesis informative and rigorous.\u003c/p\u003e\u003cp\u003eTo bring comfort to the practices of reproducibility and openness, we have detailed all of the methodologies and analysis choices made during this evaluation, which are included within the supplementary material. Such openness does not only add validity to our results but also assists coming research to replicate, validate, or build upon our study\u0026mdash;thus aiding in research advancement in the implementation of IoT and cloud computing in micronutrient surveillance.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e2.10. Reporting Bias\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe analysis of sample characteristics across the reviewed studies reveals a strong focus on IoT and micronutrients monitoring systems. During the compilation of information when writing this literature study, the PRISMA guidelines were followed throughout, to try not being bias when selecting the reports to use and avoid being bias from selectively reported results. Any paper that is related micronutrients monitoring, IoT and cloud computing will be considered and reported. After reporting and compilation of these reports, reports and information that suggests or shows traces of reporting bias will be removed and not be reported on the final publication. Multiple databases including Google scholar, SCOPUS and Web of Science, which use slightly different search strings and give out some papers which might not be available in the other databases. The resulting papers from these databases are all peer-reviewed and fact checked by published authors. A third party was consulted and asked to review our paper and check for imbalances during reporting. These eliminations of bias reporting will help avoid being bias when collecting and writing data on this subject. This makes the research more transparent so that future syntheses can more accurately assess both the promise and the limitations of IoT\u0026ndash;cloud approaches in micronutrient monitoring.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. Study selection\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the distribution of data sources utilised in this systematic review. The majority of the literature (50.00%) was retrieved from Google Scholar, reflecting its broad indexing scope and accessibility for multidisciplinary searches (Khabsa \u0026amp; Giles, 2014). Web of Science contributed 33.33% of the included studies, offering curated, high-quality peer-reviewed sources (Falagas et al., 2008), while SCOPUS accounted for 16.67%, providing comprehensive bibliographic coverage with advanced citation tracking capabilities (Burnham, 2006). This distribution underscores the importance of combining multiple databases to ensure both breadth and depth in literature retrieval, thereby minimising publication bias and enhancing the comprehensiveness of the review.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2. Study charecteristics\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e presents the temporal distribution of published papers included in this review, illustrating trends in research activity over time. The earliest relevant studies date back to 2016, with gradual increases observed in subsequent years. A notable surge in publications appears from 2019 onwards, coinciding with the growing integration of IoT and cloud computing in environmental and agricultural monitoring (Miorandi et al., 2012; Atlam \u0026amp; Wills, 2020). Peaks in 2020 and 2023 indicate heightened scholarly interest, potentially driven by advancements in sensor technology, machine learning, and the increasing urgency of addressing micronutrient monitoring in both agricultural and public health contexts. This temporal trend underscores the field\u0026rsquo;s rapid evolution and the need for continuous synthesis of emerging evidence.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e depicts the distribution of publication types represented in the systematic review. The majority of included studies were peer-reviewed journal articles (72.22%), reflecting the dominance of formal, rigorously evaluated research in the field (Ware \u0026amp; Mabe, 2015). Conference proceedings accounted for 25.00%, indicating the active role of conferences in disseminating emerging findings and technical innovations before journal publication (Franceschet, 2010). A small proportion of studies (2.78%) originated from book chapters, which often provide conceptual or case study insights. This distribution suggests that while peer-reviewed journals remain the primary vehicle for scholarly communication on IoT\u0026ndash;cloud integration for micronutrient monitoring, conferences play a significant role in the early visibility of novel approaches and prototypes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e shows the distribution of water quality sensors employed in the studies, categorised by measurement type. Chemical parameter sensors dominated, comprising 43.90% of the total, with pH sensors, copper ion electrodes, and total dissolved solids meters among the most common. These instruments are essential for detecting nutrient levels, ion concentrations, and other chemical properties critical for environmental and agricultural water monitoring (Rode et al., 2016). Multiparameter sensors accounted for 17.54%, offering integrated monitoring of multiple water quality metrics within a single device, thus improving efficiency and reducing maintenance requirements (Banna et al., 2014). Physical parameter sensors, such as turbidity and water level sensors, made up 3.08%, primarily used for sediment load and hydrological assessments. A notable 35.03% of the studies did not specify the sensor type, indicating a significant gap in reporting standards that may affect reproducibility and meta-analytical synthesis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e depicts the distribution of microcontroller and development boards used in the included studies. Arduino-based platforms dominated, representing 52.78% of usage, reflecting their affordability, ease of programming, and broad adoption in IoT prototyping for environmental monitoring (Banzi \u0026amp; Shiloh, 2014). Raspberry Pi systems accounted for 25.00%, offering greater computational capacity and suitability for data-intensive applications, including real-time analytics and machine learning integration (Halfacree \u0026amp; Upton, 2016). ESP32 devices contributed 13.89%, valued for their built-in Wi-Fi/Bluetooth capabilities, making them well-suited for wireless sensor networks. Other boards, such as the STM32F411RE (2.78%) and TelosB with CC2420 transceiver (2.78%), were less common but highlighted for their low-power operation in field deployments. Notably, 19.44% of studies did not specify the hardware platform, indicating a potential gap in reporting that limits reproducibility and technical comparability across studies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e shows the distribution of communication technologies utilised in the studies, categorised by network type. Cellular communication technologies (all GSM variants) dominated with 46.43% of usage, reflecting their reliability and broad coverage in remote and rural monitoring contexts (Raza et al., 2017). IoT platforms and protocols accounted for 17.86%, enabling seamless device interconnection and cloud integration for real-time data transmission. Low-power wide-area networks (LPWAN), represented by LoRa, made up 17.86%, valued for long-range, energy-efficient communication suited to battery-powered field devices. Short-range wireless technologies such as Zigbee (7.14%) were applied in localised sensor networks, while wired/wireless LAN connections (3.57%) were rare, primarily in fixed-location monitoring setups. Notably, 7.14% of studies did not specify the communication method, suggesting a gap in methodological transparency that could affect replicability.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e presents the distribution of cloud computing platforms and integrations reported in the reviewed studies. Unspecified platforms formed the largest proportion (25.00%), indicating a considerable reporting gap that can hinder reproducibility and hinder platform-specific performance evaluation. Among identified services, AI-enhanced cloud solutions (AI-driven cloud computing for image processing, AI APIs, and cloud-based AI/ML) accounted for 14.29%, reflecting the growing role of artificial intelligence in automating data analysis and decision-making (Marjani et al., 2017). Cloud service providers such as Amazon Cloud and AWS IoT Core comprised 10.71%, supporting large-scale IoT deployments with robust data handling capabilities. Data storage services (cloud databases, cloud storage, SQL Server, and Firebase) represented 17.86%, highlighting the importance of secure, scalable repositories for high-frequency sensor data. IoT-focused cloud services (IoT-integrated cloud computing and WSN-cloud integrations) contributed 7.14%, demonstrating targeted architectures optimised for sensor-driven monitoring systems. This distribution indicates both a diversification of cloud adoption strategies and a need for more precise methodological documentation in future studies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis systematic review demonstrates that integrating IoT and cloud computing technologies presents significant opportunities for enhancing micronutrient monitoring in environmental and agricultural systems. Quantitative analysis of the literature revealed strong research contributions from Asia, particularly India, with surface water being the most frequently monitored resource and chemical parameter sensors dominating measurement approaches. Arduino-based hardware and GSM communication technologies were the most common implementation choices, while a substantial proportion of studies failed to specify cloud platforms, indicating a gap in reporting standards. Despite promising advancements, challenges remain in addressing data volume management, energy efficiency, latency, interoperability, and security\u0026mdash;particularly in remote and resource-limited settings. The growing integration of AI and edge\u0026ndash;cloud architectures offers pathways for improving analytical capabilities, operational efficiency, and decision-making accuracy. Future research should prioritise developing robust, context-aware IoT\u0026ndash;cloud frameworks, standardising reporting protocols, and conducting long-term field validations across diverse geographies. By closing these gaps, IoT\u0026ndash;cloud solutions can play a central role in delivering scalable, cost-effective, and sustainable micronutrient monitoring systems, ultimately supporting improved agricultural productivity and public health outcomes.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMahapatro PK, Panigrahi R, Padhy N (2024) Integrated Internet of Things and Artificial Intelligence System for Real-Time Multi-Nutrient Water Quality Analysis in Agriculture. Engineering Proceedings. ; 82(1):72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ecsa-11-20358\u003c/span\u003e\u003cspan address=\"10.3390/ecsa-11-20358\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarrido-Momparler V, Peris M (2022) Smart sensors in environmental/water quality monitoring using IoT and cloud services. Trends Environ Anal Chem 35:e00173. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.teac.2022.e00173\u003c/span\u003e\u003cspan address=\"10.1016/j.teac.2022.e00173\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWiryasaputra R, Huang C-Y, Lin Y-J, Yang C-T (2024) Sensors 24(4):1180. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/s24041180\u003c/span\u003e\u003cspan address=\"10.3390/s24041180\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. An IoT Real-Time Potable Water Quality Monitoring and Prediction Model Based on Cloud Computing Architecture\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrapti DR, Shariff M, Man ARC, Ramli H, Perumal NM, T., Shariff M (2022) Internet of Things (IoT)-based aquaculture: An overview of IoT application on water quality monitoring. Reviews Aquaculture 14(2):979\u0026ndash;992\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBhati A, Hiran KK, Vyas AK, Mijwil MM, Aljanabi M, Metwally ASM, Ahmad H (2024) Low cost artificial intelligence Internet of Things based water quality monitoring for rural areas. Internet Things 27:101255. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.iot.2024.101255\u003c/span\u003e\u003cspan address=\"10.1016/j.iot.2024.101255\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlshami E, Ali M, Elsayed AEE, Eltoukhy, Zayed T (2024) IoT Innovations in Sustainable Water and Wastewater Management and Water Quality Monitoring: A Comprehensive Review of Advancements, Implications, and Future Directions, in IEEE Access, vol. 12, pp. 58427\u0026ndash;58453. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/ACCESS.2024.3392573\u003c/span\u003e\u003cspan address=\"10.1109/ACCESS.2024.3392573\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdebayo AI, Olubanjo KT, Fadeke AM, Uyanah JJ, Zirra AT, Igbaoreto WA, Fakoyede PD (2025) FROM STATIC SAMPLING TO DYNAMIC INSIGHTS: THE FUTURE OF WATER QUALITY MONITORING WITH SENSORS, IOT, AND DRONES. Sci World J 20(1):454\u0026ndash;466\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSadeeq MM, Abdulkareem NM, Zeebaree SRM, Ahmed DM, Sami AS, Zebari RR (2021) IoT and Cloud Computing Issues, Challenges and Opportunities: A Review, Qubahan Academic Journal, vol. 1, no. 2, pp. 1\u0026ndash;8, [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48161/qaj.v1n2a36\u003c/span\u003e\u003cspan address=\"10.48161/qaj.v1n2a36\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKadam D, Bhosale RS, Patil SS Multidisciplinary Model for Smart Agriculture using Internet-of-Things (IoT), International Journal of Innovative Research in Technology, vol. 12, no. 2, pp. 10\u0026ndash;15, Feb. 2023. [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchgate.net/publication/368574305\u003c/span\u003e\u003cspan address=\"https://www.researchgate.net/publication/368574305\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBotta A, De Donato W, Persico V, Pescap\u0026eacute; A Integration of Cloud computing and Internet of Things: A survey, Future Generation Computer Systems, vol. 56, pp. 684\u0026ndash;700, Mar. 2016. [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.future.2016.11.031\u003c/span\u003e\u003cspan address=\"10.1016/j.future.2016.11.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlbahri SA et al (2019) A Survey on Internet of Things and Cloud Computing for Healthcare, Electronics, vol. 8, no. 7, p. 768, [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/electronics8070768\u003c/span\u003e\u003cspan address=\"10.3390/electronics8070768\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSelvanarayanan R et al Empowering coffee farming using counterfactual recommendation based RNN driven IoT integrated soil quality command system, \u003cem\u003eScientific Reports\u003c/em\u003e, vol. 14, no. 1, p. 6269, Mar. 2024. [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nature.com/articles/s41598-024-56954-x\u003c/span\u003e\u003cspan address=\"https://www.nature.com/articles/s41598-024-56954-x\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhanyi M, Xaba S, Mlotshwa N, Thango B, Matshaka L (2024) The Role of Data Networks and APIs in Enhancing Operational Efficiency in SME: A Systematic Review. Preprints 2024100848\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSofianopoulos S, Faka A, Chalkias C, SDI-Enabled Smart, Governance (2025) A Review (2015\u0026ndash;2025) of IoT, AI and Geospatial Technologies\u0026mdash;Applications and Challenges. Land 14(7):1399. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/land14071399\u003c/span\u003e\u003cspan address=\"10.3390/land14071399\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiao Y, Chen Y, Huang Y, Yan Y Surface Protection Technologies for Earthen Sites in the 21st Century: Hotspots, Evolution, and Future Trends in Digitalization, Intelligence, and Sustainability. Coatings 2025, 15(7), 855; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/coatings15070855\u003c/span\u003e\u003cspan address=\"10.3390/coatings15070855\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSkosana S, Mlambo S, Madiope T, Thango B (2024) Evaluating Wireless Network Technologies (3G, 4G, 5G) and Their Infrastructure: A Systematic Review. Preprints 2024101331\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu W, Zhou X, Wang D, Dong J (2025) The Development and Construction of City Information Modeling (CIM): A Survey from Data Perspective. Appl Sci 15(9):4696. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/app15094696\u003c/span\u003e\u003cspan address=\"10.3390/app15094696\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAldossary D, Aldahasi E, Balharith T, Helmy TA (2025) Systematic Literature Review on Load-Balancing Techniques in Fog Computing: Architectures, Strategies, and Emerging Trends. Computers 14(6):217. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/computers14060217\u003c/span\u003e\u003cspan address=\"10.3390/computers14060217\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJ\u0026oslash;rgensen B, Ma Z (2025) Impact of EU Laws on the Adoption of AI and IoT in Advanced Building Energy Management Systems: A Review of Regulatory Barriers, Technological Challenges, and Economic Opportunities. Buildings 15(13):2160. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/buildings15132160\u003c/span\u003e\u003cspan address=\"10.3390/buildings15132160\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMtjilibe T, Rameetse E, Mgwenya N, Thango Bonginkosi, Exploring the Challenges and Opportunities of Social Media for Organizational Engagement in SMEs: A Comprehensive Systematic Review (July 06, 2024). Available at SSRN: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ssrn.com/abstract=4998542 or\u003c/span\u003e\u003cspan address=\"https://ssrn.com/abstract=4998542 or\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.2139/ssrn.4998542\u003c/span\u003e\u003cspan address=\"10.2139/ssrn.4998542\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMishra S, Shanmugam B, Yeo K, Thennadil S, SDN-Enabled IoT (2025) Secur Frameworks\u0026mdash;A Rev Existing Challenges Technol 13(3):121. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/technologies13030121\u003c/span\u003e\u003cspan address=\"10.3390/technologies13030121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhoa E, Kassahun A, Verdouw C, Tekinerdogan B Challenges and Solution Directions for the Integration of Smart Information Systems in the Agri-Food Sector. Sens 2025, 25(8), 2362; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/s25082362\u003c/span\u003e\u003cspan address=\"10.3390/s25082362\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBank\u0026oacute; M, Dyszewski S, Kr\u0026aacute;lov\u0026aacute; M, Limpek M, Papaioannou M, Choudhary G, Dragoni N (2025) Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges. Algorithms 18(4):209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/a18040209\u003c/span\u003e\u003cspan address=\"10.3390/a18040209\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThobejane LT, Thango BA (2024) Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review. Appl Sci 14:6097. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/app14146097\u003c/span\u003e\u003cspan address=\"10.3390/app14146097\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFernando X, Lăzăroiu G (2024) Energy-Efficient Industrial Internet of Things in Green 6G Networks. Appl Sci 14(18):8558. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/app14188558\u003c/span\u003e\u003cspan address=\"10.3390/app14188558\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndriulo F, Fiore M, Mongiello M, Traversa E, Zizzo V Edge Computing and Cloud Computing for Internet of Things: A Review. Inf 2024, 11(4), 71; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/informatics11040071\u003c/span\u003e\u003cspan address=\"10.3390/informatics11040071\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDanil L, Jahroh S, Syarief R, Taryana A Technological Innovation in Start-Ups on a Pathway to Achieving Sustainable Development Goal (SDG) 8: A Systematic Review. Sustain 2025, 17(3), 1220; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su17031220\u003c/span\u003e\u003cspan address=\"10.3390/su17031220\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarques P, V\u0026aacute;z P, Silva J, Martins P, Abbasi M (2025) Real-Time Gesture-Based Hand Landmark Detection for Optimized Mobile Photo Capture and Synchronization. Electronics 14(4):704. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/electronics14040704\u003c/span\u003e\u003cspan address=\"10.3390/electronics14040704\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThango BA, Obokoh L (2024) Techno-Economic Analysis of Hybrid Renewable Energy Systems for Power Interruptions. Syst Rev Eng 5(3):2108\u0026ndash;2156. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/eng5030112\u003c/span\u003e\u003cspan address=\"10.3390/eng5030112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParaskevas A, Madas M, Zeimpekis V, Fouskas K (2024) Smart Ports in Industry 4.0: A Systematic Literature Review. Logistics 8(1):28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/logistics8010028\u003c/span\u003e\u003cspan address=\"10.3390/logistics8010028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCacciuttolo C, Guzm\u0026aacute;n V, Catri\u0026ntilde;ir P, Atencio E (2024) Sensor Technologies for Safety Monitoring in Mine Tailings Storage Facilities: Solutions in the Industry 4.0 Era. Minerals 14(5):446. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/min14050446\u003c/span\u003e\u003cspan address=\"10.3390/min14050446\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Internet of Things (IoT), Cloud Computing, Micronutrient Monitoring, Environmental Sensing, Precision Agriculture, Data Integration, Edge Computing","lastPublishedDoi":"10.21203/rs.3.rs-7335820/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7335820/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe convergence of Internet of Things (IoT) and cloud computing offers a transformative approach to micronutrient monitoring in environmental and agricultural systems. As IoT devices generate continuous data streams, cloud platforms provide scalable resources for real-time processing, analysis, and storage. This systematic review, conducted under PRISMA 2020 guidelines, examined 36 studies on IoT\u0026ndash;cloud integration for micronutrient detection. Most studies were sourced from Google Scholar (50.00%), Web of Science (33.33%), and SCOPUS (16.67%). Peer-reviewed journal articles dominated (72.22%), with Asia contributing the highest share of research (50.00%), led by India (30.56%). Surface water was the most monitored source (38.89%), followed by treated water (19.44%) and groundwater (13.89%). Chemical parameter sensors were most common (43.90%), and Arduino platforms were the predominant hardware (52.78%), with GSM communication technologies leading (46.43%). Unspecified cloud platforms accounted for 25.00%, while AI-enhanced cloud solutions represented 14.29%. Core challenges identified include data volume, energy constraints, latency, interoperability, and security vulnerabilities, particularly in remote settings. The findings highlight the need for robust, context-aware IoT\u0026ndash;cloud frameworks, improved reporting standards, and the adoption of AI and edge\u0026ndash;cloud architectures to enhance sustainable, data-driven decision-making in precision micronutrient management.\u003c/p\u003e","manuscriptTitle":"Technological Integration for Micronutrient Monitoring in Water Systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 11:36:59","doi":"10.21203/rs.3.rs-7335820/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"64b2bc7b-79dc-4c67-8d0e-ae4054e286a0","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52917454,"name":"Electrochemistry"},{"id":52917455,"name":"Marine and Freshwater Ecology"},{"id":52917456,"name":"Marine and Freshwater Biology"},{"id":52917457,"name":"Electrical Engineering"}],"tags":[],"updatedAt":"2025-08-12T11:36:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 11:36:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7335820","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7335820","identity":"rs-7335820","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}