Sustainable Automated Vertical Farming

Sustainable Automated Vertical Farming | 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 Article Sustainable Automated Vertical Farming Prateek Barve, Mridulay Dixit, Sritama Roy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4948307/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This study offers a thorough strategy for automated vertical farming that is sustainable in response to the urgent problems that traditional agricultural methods are currently facing. The primary objective is to design and implement a real-time monitoring and alert system which is capable of optimizing growing conditions and ensuring the long-term viability of vertical farming operations. The system architecture revolves around the integration of sensor technology, automation, and data analytics to enable proactive management of environmental parameters crucial for plant growth. Specifically, sensors are connected to a microcontroller to capture data on temperature, humidity, motion, light intensity, fire state, air quality, and soil moisture. This data is continuously monitored and analyzed in real-time to detect anomalies and deviations from optimal conditions. Upon detecting abnormal conditions such as low light intensity, fire risks, low soil moisture, or unstable rack conditions, the system triggers alerts via SMS using the Twilio service. These alerts are sent to some designated recipients, allowing for timely interventions to rectify the situation and prevent potential crop loss. The proposed system aims to address key gaps and areas of improvement in current vertical farming practices. The results or potential benefits involve reduction in energy costs and water usage based on analytic report custom to farmer, reducing operational expenses and trade it with one-time capital expense by means of reduction in labor costs cut by 40-60% (estimated) and further help to avoid skill issue concern of the labors. The crop production process is made more efficient and optimized with precise use of natural resources which in-turn makes the system more sustainable and improves crop yield and quality. The process gets automated by using actuators which rectify or act on emergency alerts instantly, therefore increasing reliability and propose customization to the customer. By providing a proactive approach to environmental management, it seeks to enhance resource efficiency, crop quality, and overall productivity. Moreover, by leveraging advanced data analytics techniques, the system offers insights into environmental trends and patterns, enabling informed decision-making and adaptive responses. Overall, the system represents a significant step towards the realization of sustainable automated vertical farming. By harnessing the power of sensor technology, automation, and data analytics, it offers a scalable and efficient solution to the challenges facing modern agriculture. It can fully realize vertical farming's potential as a resilient and sustainable food production system for the future use. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Energy science and technology Sustainable agriculture Vertical farming Sensor technology Data analytics Internet of Things (IoT) Hydroponics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1 Introduction The dawn of the 21st century brings with it a myriad of challenges to traditional agricultural practices. Rapid population growth, urbanization, and climate change are placing unprecedented strains on human ability to feed the world's population while maintaining environmental sustainability [1]. In this context, the concept of vertical farming emerges as a beacon of hope, offering a revolutionary approach to agricultural production that is both efficient and environmental friendly [2]. Because traditional agriculture depends on large areas of land, it is becoming less and less viable in the face of urbanization and dwindling arable land [3]. Moreover, conventional farming methods often entail significant water usage, chemical inputs, and transportation costs, contributing to environmental degradation and greenhouse gas emissions [4]. It is critical to investigate alternate methods of producing food that can both meet the growing demand and lessen the impact on the environment as the world's population keeps growing [5]. The concept of agriculture itself is being challenged by the disruptive innovation known as vertical farming. At its core, crops are grown in vertically stacked layers in vertical farming, usually in controlled interior spaces like skyscrapers or warehouses [6]. The concept of Vertical Farming itself differentiates the thing from other traditional methods of farming by: a) Improving Space Efficiency (higher yield in less space) - The vertically stacked farms can produce 15 to 100 times more number of plantations per square foot [6]. b) It allows us to implement urban agriculture where farms can be set up in cities and reducing the reliance on distant farmlands for easy and quick delivery, thus reducing prices due to cost cutting in transportation. Compared to conventional farming techniques, vertical farms can achieve larger crop yields with less water and land use by utilizing cutting-edge technologies like Light Emitting Diode (LED) lighting, aeroponics, and hydroponics. This paradigm shift holds the promise of transforming the way human being produces food, making it more sustainable, resilient, and accessible to urban populations. The motivation behind the vertical farming extends beyond achieving more efficiency and productivity gains. It embodies a fundamental shift in relationship with food and the environment, emphasizing principles of sustainability, resilience, and self-sufficiency. Vertical farming lowers the need for long-distance transportation by bringing food production closer to urban areas, which lowers carbon emissions and decreases food waste [7], [8]. Moreover, as vertical farms are weather-independent, they can run all year round, providing a steady and dependable supply of food even in the face of disruptions brought on by climate change. Moreover, vertical farming presents an opportunity to tackle urgent social and economic issues, especially in densely populated urban areas. By creating local employment opportunities and revitalizing vacant urban spaces, vertical farming contributes towards the community development and economic growth [9]. Additionally, vertical farms can serve as educational hubs, raising awareness about the importance of healthy eating, environmental supervising, and sustainable agriculture [10]. Few challenges in traditional methods are: a) High operational cost that involves unnecessary high energy consumption by lighting systems (tend to work even if not required), sprinklers, humidifiers and the dust suppression systems installed. b) Recurring manual labor costs to operate the watering systems for plants, handling temperature and humidity control systems manually. There is also a significant challenge to find a suitable skilled labor as per the requirement. c) No real time measure of crop growing parameters like temperature, humidity, air quality, light intensity, moisture present in the soil, pest detection, fire detection, rack stability. d) No measured usage of natural resources like water, light, pesticides and fertilizers making the crop growing process quiet unsustainable. e) No analytics and automation performed to allow system to be automated and make data driven decisions thereby making it more reliable. f) No relevant solutions against seasonal constraints such as flooding, drought. g) No proper/efficient resource allocation for optimized crop production. All these challenges imply lack of real time monitoring, automation, analytic and optimized decision making. Technical barriers such as high upfront costs, energy requirements, and complex logistic considerations pose significant obstacles to widespread adoption [11]. Furthermore, concerns about the long-term viability, profitability, and scalability of vertical farming operations are still unresolved. With the potential to completely transform agricultural methods, vertical farming offers a special chance to leverage data analytics and cutting-edge technology to improve sustainability and streamline operations. By integrating cutting-edge sensor networks, Internet of Things (IoT) devices, vertical farms can unlock a wealth of insights and capabilities that can significantly improve their efficiency, productivity, and environmental impact. Precision Monitoring and Control: When it comes to precise monitoring and controlling of vertical farming environments, data analytics can be extremely important. By placing a network of sensors throughout the growth chambers, critical parameters including temperature, humidity, light intensity, nutrient levels, and air quality can all be continuously monitored real-time. The abundance of data can be used to find the best growing environments, spot irregularities, and proactively modify environmental parameters to increase crop yields and quality. The study allows crop production to be climate independent allowing crops to be grown 24/7 throughout the year with no concerns of seasonal constraints like drought, flooding or temperature fluctuations. The study approaches waste minimization by detecting early stage of threat to plantations like of rodents, pests, fires and is acted upon instantly unlike other vertical farming approaches where the delay in response leads to 10-15% of crop wastage, following automated nutrient recycling that reduces fertilizer waste up to 50%. Further, automated harvesting techniques can reduce up to 20% crop wastage [12]. Automated Irrigation and Nutrient Management: The major improvement follows on significant reduction in wastage and usage of water resource by implementing practices like Hydroponic, aeroponic and aquaponic systems of water re-cycling and prevents issues or soil erosion (prevents top soil depletion). The study approaches water usage optimization by implementing precision irrigation like Hydroponics & Aeroponics that uses 95% less water than traditional farming. (embedded in the sprinklers). Real-time moisture monitoring also ensures proper utilization of water resources and prevent over-watering [13]. Real-time monitoring can further enhance the aspect by enabling automated and intelligent irrigation and nutrient management systems. Sensor data can be used to precisely monitor moisture levels, nutrient concentrations, and plant growth rates, allowing for the development of adaptive algorithms that optimize water and nutrient delivery based on real-time plant requirements. This approach not only conserves resources but also ensures optimal plant health and yield [14]. The automation helps in improved resource allocation that can help in optimizing lighting, irrigation and nutrients which is based on statistical insights. (if data shows that light intensity peaks at mid-day, the system automatically can reduce LED usage during that time). Energy Optimization: Artificial lighting and climate control systems are integral to vertical farming operations and can greatly increase energy consumption and costs. The implemented system is independent of source type of energy or power (on-grid/off-grid solar, regular supply). Irrespective of the source, in general it aims to reduce the power consumption (a part of the power source) by the use automation for resource allocation and environment monitoring control that reduces recurring labor costs associated. It involves the use of standard sensor and actuator modules that brings down the one-time capital expenditure. The study approaches energy consumption by following practices like, IoT controlled actuation or control system. For example, conventional vertical farms use fixed-intensity LEDs, leading to wastage of energy which is not the case here (dynamic lighting). The study uses automatic spectrum tuning and smart dimming reducing energy use by 30-50%, use of smart industry level sensor nodes with optimized Heating, Ventilation, and Air Conditioning (HVAC) cycles instead of running the sensors constantly. The system can also use integrated solar panels and battery storage for energy stability. Data analytics can play a crucial role in optimizing energy usage by analyzing historical data, identifying energy-intensive processes, and implementing predictive algorithms to anticipate and adjust energy usage based on factors such as crop growth stages, weather patterns, and occupancy levels [15]. Pest and Disease Management: Vertical farms, despite their controlled environments, are not immune to pest infestations and plant diseases. Real-time monitoring and data analytics can aid in the early detection and prevention of these threats by analyzing sensor data, identifying patterns, and recognizing potential signs of infestation or disease before they become widespread [16]. Yield Prediction and Optimization: A major objective of vertical farming is to increase crop yields with the least amount of resource inputs possible. Data analytics can play a pivotal role in achieving this by leveraging historical data, environmental conditions, and plant growth patterns to develop predictive models for crop yields. The system generates a correlational analysis report (and scatter plots) between various parameters to help to understand the relation between them which eventually help in balancing the parameters for ideal crop production. It helps to study how different parameter settings interact to maximize plant growth. For example, leafy greens like Spinach can thrive with humidity and moderate temperature (Data-driven adjustments). These models can then be used to optimize growing conditions, adjust nutrient levels, and implement strategies that consistently deliver high yields while minimizing waste and resource consumption. The real-time sensing and automation ensures stable food supply. Supply Chain Optimization: Due to their urban locations, vertical farms have the potential to drastically lower the transportation expenses and carbon emissions related to conventional agriculture adding to a more sustainable approach. Further it can be enhanced by optimizing supply chain logistics, enabling real-time monitoring of crop growth and yield predictions, and facilitating efficient distribution and delivery to local markets and consumers [17]. Sustainability Metrics and Reporting: Key sustainability metrics must be measured and reported on since vertical farming seeks to be a sustainable and eco-friendly solution. The collection and analysis of data pertaining to waste generation, carbon footprint, and other environmental indicators, as well as resource consumption (water, energy, and nutrients) can be a crucial function of data analytics [15]. The system generates descriptive statistics like mean, median, mode, standard deviation, variance, quartiles (1%, 25%, 50%, 75%, 99%), min, max and summary of various parameters involved. It helps to determine ambient conditions for the crop production (e.g., irrigation can be controlled based on soil moisture summary), prevent skewed analysis of data collected in real time (rectify sudden temperature hikes), identify common conditions experienced by the crops (better ventilation control if constant high humidity), measures fluctuation in environmental conditions (high stress in plant if always high temperature or humidity) for better climate regulation. This information can be used to generate comprehensive sustainability reports, identify areas of improvement, and demonstrate the environmental benefits of vertical farming to stakeholders and consumers. The study focuses primarily on optimizing and improving efficiencies of the processes involved in production of crops or plantations in precision/vertical farming. The analytics part involves concepts like correlational analysis and descriptive statistics performed on the real-time collected data by sensors, for better and automatic decision making by the actuation system. The alert system helps in close monitoring of the actual system that is implemented with detailed report generation at a specific time interval subject to the consumer. The study also focuses on reducing operational cost of vertical farming, reducing excessive usage of natural resources and energy conservation, therefore contributing to create a sustainable environment. By leveraging sensor technology and automation, the study aims to develop a real-time monitoring and alert system capable of optimizing the growing conditions, mitigating risks, and maximizing crop yields in vertical farming environments. Through interdisciplinary research and collaboration, endeavour to unlock the full potential of vertical farming as a sustainable solution to the challenges of the 21st century. 2 Literature review The rapid growth of urban population and the mounting pressures of climate change have necessitated a paradigm shift in agricultural practices. In light of these difficulties, conventional horizontal farming practices—which depend on large areas of arable land and substantial water resources—become less and less viable. This requirement has led to a growing body of research on vertical farming, a novel strategy that re-imagines food production inside carefully regulated indoor spaces. Vertical farming holds the promise of achieving higher crop yields with minimal land and water usage, while dramatically reducing transportation costs and associated carbon emissions. However, realizing the full potential of this disruptive technology requires a multidisciplinary effort, drawing upon advancements in areas such as precision agriculture, data analytics, IoT, artificial intelligence (AI), and sustainable energy systems [14]. The literature review synthesizes insights from a diverse array of studies, highlighting the theoretical underpinnings, technological enablers, cultivation techniques, and environmental considerations that underpin the development of sustainable automated vertical farming systems. By integrating these varied perspectives, researchers and practitioners alike can unlock the transformative potential of vertical farming as a resilient and resource-efficient solution to address the global food security and environmental challenges of the 21st century. Theoretical Foundations and Overviews: Beacham, A.M., Vickers, L.H., & Monaghan, J.M. (2019) have provided a comprehensive summary of approaches towards vertical farming, including hydroponic, aeroponic, and aquaponic systems. The discussion was on various structural designs such as stacked layers, rotating systems, modular units, highlight the importance of environmental control and energy efficiency. Despommier, M. et.al., (2011) [13] have introduced the concept of "vertical farm" and outlines its potential benefits, such as year-round crop production, reduced transportation costs and efficient use of land and water resources. Precision Agriculture and Data Analytics: Pierce, F.J. & Nowak, P. (1999) [14] have explored the principles of precision agriculture, emphasizing the use of GPS, remote sensing and variable rate technology to optimize inputs and maximize yields. The potential of site-specific crop management strategies enabled by data analytics was the point of discussion. Gebbers, R. & Adamchuk, V.I. (2010) [18] have described the use of proximal soil sensing and data fusion techniques to improve spatial resolution and accuracy of soil property maps enabling more precise decision-making in precision agriculture. IoT and Sensor Networks: Muangprathub, J. et. al., (2019) [19] have demonstrated the application of IoT technologies and data analytics in monitoring environmental factors including soil moisture, humidity and temperature in vertical farming setups. The proposed idea was a real-time monitoring system that can optimize growing conditions and reduce resource consumption. Ghobakhlou et. al., (2013) [20] have investigated the integration of wireless sensor networks (WSNs) in agricultural environments, showcasing their effectiveness in real-time monitoring of soil moisture, temperature and humidity, which are crucial for vertical farming operations. Machine Learning and AI: Dhanya et. al., (2021) [21] have provided insights into the use of machine learning methods in agricultural data analysis such as conventional neural network and artificial neural networks. The research group have showcased the potential of these techniques in predictive modeling and deep learning algorithms were for optimizing crop yield, pest management and resource allocation in vertical farming operations. Talaviya et. al., (2020) [22] have investigated the role of artificial intelligence (AI) in agriculture, emphasizing its potential in optimizing resource allocation through precision farming, crop management through predictive analytics and yield prediction through machine learning models tailored for vertical farming contexts. Energy and Lighting Optimization: Gonzalez et. al., (2021) [23] have shaded light on the importance of sustainable lighting technologies such as LED, in reducing energy consumption and optimizing plant growth in vertical farming environments. The performance of different LED configurations and their impact on crop yield and quality has huge impact. Kozai, T. et al. (2006) [24] explored energy-saving techniques for environmentally friendly plant factories such as optimizing air conditioning, dehumidification systems, implementing thermal insulation and integrating renewable energy sources. Environmental Control and Optimization: Wang et. al., (2024) [25] have explored the optimization of environmental parameters in vertical farming through advanced control algorithms such as model predictive control (MPC) and fuzzy logic control. The demonstration was based on the potential of these techniques in maintaining optimal growing conditions while minimizing energy consumption and resource waste. Farhangi et. al., (2023) [26] have elucidated the trade-offs between various environmental parameters (e.g., temperature, humidity, light intensity) and crop yield, providing insights into the development of multi- objective optimization strategies for sustainable vertical farming. Cultivation Techniques and Systems: Ali AlShrouf (2017) [27] have demonstrated the potential of soil-less farming methods such as hydroponics and aeroponics, in conserving water, maximizing space utilization and enhancing crop productivity in vertical farming environments. The research group have compared the performance of different hydroponic systems and nutrient delivery methods. Bambhaniya et. al., (2023) [28] have investigated the applications of aquaponics in vertical farming, combining fish and plant cultivation in a closed-loop system for efficient resource cycling and nutrient management. Sustainability and Resilience: Ang et.al., (2022) [29] have highlighted the importance of sustainable energy solutions such as renewable energy integration (e.g., solar, wind), in reducing carbon footprint and enhancing energy resilience in agricultural operations, including vertical farming and finally have evaluated the feasibility and economic viability of different renewable energy sources for vertical farming facilities. Zheng et. al., (2024) [30] have explored climate-smart agriculture strategies for vertical farming, providing insights into resilient farming practices in the face of climate change uncertainties. The proposed adaptive management strategies such as drought-tolerant crop varieties and water-efficient irrigation systems were used to mitigate the impacts of climate change on vertical farming operations. Microbiome and Nutrient Management: Vimal et al. (2024) [31] have elucidated the role of microbial communities in nutrient cycling, disease suppression, plant growth promotion and underscoring the importance of microbiome management for sustainable crop production in vertical farming and have investigated the effects of different cultivation techniques as well as environmental conditions on the microbial diversity and composition in vertical farming systems. Fathidarehnijeh et. al., (2024) [32] have looked into hydroponic system nutrient solution management techniques, providing insightful information on the way to maximize nutrient delivery and reduce waste in vertical farming operations. It was assessed how various nutrient formulations and replenishment techniques affected crop yield and quality. Supply Chain and Blockchain: Guangjie et al. (2023) [33] have investigated the application of blockchain technology in agricultural supply chains, emphasizing its potential in enhancing transparency, traceability and trustworthiness in vertical farming operations. The proposed system was a blockchain-based traceability system that can track the provenance of crops, monitor environmental conditions and ensure food safety. Munim et.al., (2024) [34] have explored the concept of "blockchain-enabled Internet of Things (BIoT)" and its applications in smart agriculture, including vertical farming. In vertical farming ecosystems, the potential of blockchain technology has been benefited to enable safe data sharing, automate transactions and streamline supply chain management. Social and Economic Aspects: Benis et. al., (2016) [35] have analyzed the economic viability and environmental impacts of urban vertical farming providing insights into the feasibility and scalability of the emerging agricultural model. A life cycle assessment and cost-benefit analysis were to evaluate the sustainability and profitability of different vertical farming scenarios. Reducing losses and waste, cutting transportation distances and accounting for technological advancements could be all important ways to improve the food supply chain's efficiency and potentially mitigate its effects. Verma et.al., (2024) [36] have explored the social and economic dimensions of vertical farming, highlighting its potential in addressing food security, job creation and community development in urban areas. The challenges and opportunities associated with integrating vertical farming into urban planning and policy frameworks was discussed. The review highlights the multidisciplinary nature of sustainable automated vertical farming, encompassing diverse fields such as agriculture, engineering, data science, environmental science and economics. By integrating insights from these varied domains, researchers and practitioners can unlock the full potential of vertical farming as a sustainable and resilient solution to address the global food security and environmental challenges of the 21st century. Table 1: A systematic literature review of the recent agricultural approaches. Sr. No. Title Authors Publication Year Summary References 1 Vertical farming: a summary of approaches to growing skywards Beacham , A.M., Vickers, L.H., & Monagh an, J.M. 2019 Increasing crop yield per square metre of land is the aim of vertical farming (VF) techniques. Nonetheless, a variety of endeavours have been characterized by this word, ranging from massive skyscrapers utilized for the industrial cultivation of an extensive array of crops to small-scale or communal vegetable and herb gardening. [37] 2 The Rise of Vertical Farms Dickson Despommier 2009 Introduces the concept of "vertical farm" and its potential benefits. [38] 3 Aspects of Precision Agriculture Pierce, F.J. & Nowak, P. 1999 Explores precision agriculture principles and data-driven decision-making. [39] 4 Precision Agriculture and Food Security Gebbers, R. & Adamch uk, V.I. 2010 Examines proximal soil sensing and data fusion techniques. With precision agriculture, one can control the amount and quality of agricultural products produced while keeping an eye on the food production chain. [40] 5 IoT-enabled system for monitoring and controlling vertical farming operations Harn Tung Ng, Zhi Kean Tham, Nurul Amani Abdul Rahim, Ammar Wafiq Rohim, Wei Wen Looi, Nur Syazreen Ahmad 2023 The proposed system can offer several benefits such as real-time environmental condition monitoring and control, reduced energy consumption, increased scalability, flexibility, support for sustainable and effective food production. [41] 6 A decision support framework for the design and operation of sustainable urban farming systems Lanyu Li, Xian Li, Clive Chong, Chi-Hwa Wang, Xiaonan Wang 2020 The suggested framework is built on a comprehensive system model that takes into account the material and energy consumption during the production of vegetables as well as the environmental and economic outcomes of urban farming systems. [42] 7 Pulsed LED‐Lighting as an Alternative Energy Savings Tech‐ nique for Vertical Farms and Plant Factories Ernesto Olvera‐Gonzalez, Nivia Escalante‐Garcia, Deland Myers, Peter Ampim, Eric Obeng, Daniel Alaniz‐Lumbreras, and Victor Castaño 2021 Energy conservation in systems is proposed for closed plants for production system (CPPS). Vertical farms to improve utilisation efficiency and save costs without having a negative effect on crop or plant productivity. [23] 8 A Comprehensive Review on Sustainable Industrial Vertical Farming Using Film Farming Technology Zitian Zhang, Michel Rod, Farah Hosseinian 2020 Vertical farming, or VF provides an innovative solution to various problems. VF uses stacks of growing racks and beds to maximise grow space per square foot of land. Water conservation is the usual goal of hydroponics. [43] 9 Vertical farming - smart urban agriculture for enhancing resilience and sustainability in food security Soojin Oh et. al., 2022 It talks about the way modern Internet of Things (IoT) applications have led to the development of sophisticated precision monitoring and controlling systems for vertical farming. [44] 10 Energy-efficient automated vertical farms Maxence Delorme, Alberto Santini 2022 Autonomous vertical farms (VFs) are becoming more and more popular because they produce food with less water and pesticides than traditional agricultural methods because they yield much more per square metre. Three Mixed-Integer Linear Programming (MIP) formulations, along with a Constraint Programming model and valid inequalities, are proposed. [45] 11 LED Lighting in Vertical Farming Systems Enhances Bioactive Compounds and Productivity of Vegetables Crops Cinthia Nájera, Victor M. Gallegos-Cedillo, Margarita Ros and José Antonio Pascual 2022 This study aimed to perform a bibliometric analysis on the benefits of vertical farming production systems on the nutraceutical quality parameters of horticultural crops. In addition, the main metrics that are employed to evaluate crop quality and yield are identified. [46] Observations from the Literature Review: Table 1 is discussed about the literature highlights the immense potential of vertical farming to revolutionize agricultural practices, driven by the integration of cutting-edge technologies and data-driven approaches. Theoretical frameworks and overviews underscore the multifaceted benefits of vertical farming including efficient resource utilization, reduced environmental impact and year-round crop production. However, realizing these benefits hinges on the effective deployment of precision agriculture techniques, IoT sensor networks and advanced data analytics for real-time monitoring, predictive modelling and optimization of growth conditions. Machine learning and AI algorithms show promise in optimizing resource allocation, crop management and yield prediction while sustainable energy solutions such as LED lighting and renewable energy integration addressing the energy demands of controlled environments. Environmental control strategies and leveraging techniques like model predictive control and multi-objective optimization aim to balance resource efficiency with optimal plant growth. Innovative cultivation systems like hydroponics, aeroponics and aquaponics are coupled with microbiome management and nutrient solution strategies that offers pathways to sustainable soilless farming. Furthermore, blockchain technology and climate-smart agriculture practices enhance supply chain transparency, traceability and resilience to climate uncertainties. Economic viability assessments and explorations of social dimensions highlight the potential of vertical farming in addressing food security, job creation and community development, particularly in urban areas. 3 Proposed Methodology In the pursuit of sustainable automated vertical farming, a meticulous methodology is paramount to ensure the efficient collection, analysis and utilization of environmental data. This section outlines the sequential steps that are involved in putting the monitoring and alert system into place. It clarifies how different sensors are deployed, how data is collected, how analysis is done, and how alert mechanisms work. The block diagram of the proposed system is shown in Fig. 1. The system involves the use of 9 different types of sensors that include Precipitation sensor (for detecting rain water amount), soil moisture sensor, temperature and humidity sensor, air quality measurement sensor (or gas detection sensors), light intensity measurement sensor (Light Dependent Resistor), vibration sensor (to detect any unwanted movement, earthquakes or vibrations), tilt sensor (to ensure rack stability), fire detection sensor, PIR Sensor (passive infrared sensor for rodents or pest detection installed on adjacent edges of the entry point of rack or shelves). They collect the real time data observed in the crop environment and via serial communication devices like HC-05(wireless) connected to microcontrollers like Arduino, data is transferred to the main server or computer. In the main server the python code converts serially received data format having different values to comma-separated values (CSV) format and store it in a folder. While data transferring the real-time values are standardized via standard scaler modules present and are checked for any threshold cross. If any deviation or anomaly occurs SMS Notification is sent via Twilio (3rd party service) Application Programming Interface (API) preinstalled which works on account generated serial ID, authorization token, a central call number and a list of recipient numbers. The generated data is sent to the analytics module for further processing to find or extract descriptive statics of the data including mean, median, mode, standard deviation, variance, quartile, summary, min, max. The data is also processed for correlational analysis and plots between all the necessary binary or non-binary data collected to understand the parameter dynamics and adjust it to balance the resources. Based on the statistics and relations, subject experts/matter can create suitable threshold in the system for appropriate decision making (data driven). A security code is also continuously run to detect potential threats by creating counts of the vital parameters like fire, quake, pest. For the specific timeline set by the consumer and sending (mail, SMS) reports to the concerned/registered person. The actuators involving sprinkler systems, lighting systems, dust suppression, humidifiers, ventilation system are then adjusted according to the systems requirement for crop production. The system also helps defining or analyzing stages of emergency with appropriate timestamps and possibly predict conditions to avoid them by also observing the parameters at that instant. For automation the system uses low powered serial Bluetooth communication like HC-05 compatible with any micro-controller like Arduino UNO which supports data transfers between the sensors a server and the server and actuators. Sensor Deployment and Data Collection: The foundation of any data-driven system lies in the accurate and comprehensive collection of relevant environmental data. In the context of vertical farming, a multitude of sensors are deployed to monitor key parameters essential for optimal plant growth and environmental control. Temperature Sensor: One of the fundamental parameters influencing plant metabolism and growth, the temperature sensor is strategically placed within the farming environment to capture ambient temperature variations. Humidity Sensor: Ensuring adequate humidity levels is critical for preventing desiccation and maintaining plant health. The humidity sensor continuously measures the relative humidity within the vertical farming setup. Motion Sensor: Beyond environmental parameters, security and intrusion detection are paramount in safeguarding the farming facility. Motion sensors are deployed to detect any unauthorized movement within the premises, triggering alerts if necessary. Light Intensity Sensor: Light serves as the primary energy source for photosynthesis, making it imperative to monitor light intensity levels. The light intensity sensor quantifies the illumination within the farming environment, ensuring plants receive optimal light exposure. Fire Detection Sensor: Safety is of paramount importance in any agricultural setting and fire detection sensors are employed to identify the presence of smoke or fire, triggering immediate alerts to prevent potential disasters. Air Quality Sensor: Assessing air quality is crucial for maintaining a healthy and conducive environment for plant growth. Air quality sensors measure parameters such as carbon dioxide (CO 2 ) levels and volatile organic compounds (VOCs), providing insights into indoor air quality. Soil Moisture Sensor: In hydroponic or soil-based cultivation systems, monitoring soil moisture levels is essential for ensuring proper hydration of plant roots. Soil moisture sensors gauge the moisture content in the growing medium, facilitating optimal irrigation management. The system is designed to be more scalable by adopting a modular design such as use of modular sensor nodes to expand monitoring, adopting a decentralize control system so that different levels of square foot areas or different plantations are operated independently, adopting EDGE Computing for local and real-time decisions. Apart from this, the system provides customization on different category and number of sensor nodes to the consumer as per the need or system requirement specifications. Scalability also depends on installation method involved and area of implementation or warehouse. The economic feasibility is associated with Initial Setup Costs, Energy consumption, labor cost, water and nutrient cost, Market Demand and Return on Investment (ROI). These sensors, meticulously deployed throughout the vertical farming setup, continuously collect real-time data on temperature, humidity, motion, light intensity, fire state, air quality, and soil moisture, forming the foundational dataset for subsequent analysis. Data Export to CSV: The voluminous stream of sensor data generated necessitates an efficient and structured approach for data management. To this end, the collected sensor data is processed and exported to CSV file format, a universally recognized format for tabular data storage. Timestamping: Each sensor reading is meticulously timestamped to establish a chronological sequence of data points, facilitating temporal analysis and trend identification. Data Organization: The exported CSV file is organized into distinct columns corresponding to each environmental parameter, ensuring data integrity and ease of analysis. Storage Protocol: The CSV file, containing the aggregated sensor data, is stored in a designated repository or cloud storage platform, ensuring accessibility and data integrity for subsequent analysis and archival purposes. This structured approach to data exportation facilitates seamless integration with data analysis tools and ensures the availability of a comprehensive dataset for further exploration. Data Analysis Using KNIME Software: The study uses Konstanz Information Miner (KNIME) platform for all kinds of data pre-possessing and analytics that is performed. It involves a No-Code workflow with easy to drag and drop interface, therefore minimizing the use of extensive programming and thus ideal for real-time data pipelines from sensors. It is easy to perform trend analysis, time series forecasting and correlational analysis on sensor data. It can be scheduled to run periodic reports for optimizing farm conditions. It supports seamless IoT and sensor data integration that connects to databases, IoT devices, and APIs for real-time farming system monitoring and it also supports CSV, JSON, SQL, and cloud storage formats. KNIME can collect real time data generated from the server for data cleaning, transformations and visualizations (being user friendly). Various other platforms can also be used or integrated with the system like Tableau, Power BI, Apache spark, Rapid Miner and is subject to the choice of the consumer or tool user. With the sensor data securely stored in CSV format, the next step entails in-depth analysis and exploration of the dataset to derive actionable insights and identify trends. Descriptive Statistics: Utilizing KNIME's robust statistical functionalities, descriptive statistics can be computed for each environmental parameter. These statistical measures provide a comprehensive overview of the dataset, elucidating central tendencies, variability and distribution characteristics. Correlational Analysis: The interplay between various environmental parameters is investigated through correlational analysis. Specifically, correlations between temperature and light intensity, humidity and light intensity, and other relevant pairs of parameters are examined to identify potential relationships and dependencies. Visualization Tools: KNIME's rich suite of visualization tools is leveraged to generate insightful graphical representations of the sensor data. Line plots, scatter plots, histograms and heatmaps are employed to visually depict trends, patterns, and anomalies within the dataset, enhancing interpret-ability and facilitating data-driven decision-making. The categorization of the real-time collected data is done mainly on binary (fire detected, rack stability, vibration detection, motion detection) for alert reports and mechanisms to act on and non-binary data (moisture, temperature, humidity, precipitation, light intensity, air quality) for correlational analysis or descriptive statistic or future optimal conditions predictions, predictive maintenance models to determine relations for a balanced threshold environmental conditions of the crops. Through the iterative application of these analytical techniques, a clear understanding of the vertical farming environment is attained, enabling stakeholders to discern patterns, anticipate trends and optimize farming practices accordingly. Threshold Monitoring and Alerting: Proactive monitoring of environmental parameters is imperative to preemptively identify deviations from optimal conditions and mitigate potential risks to crop health and safety. To this end, predefined threshold values are established for each environmental parameter based on agronomic best practices and safety standards. Threshold Definition: Threshold values are determined for parameters such as temperature, humidity, light intensity and soil moisture, delineating upper and lower bounds beyond which abnormal conditions are deemed to occur. Alert Triggering Mechanism: The sensor data is continuously monitored in real-time and if any parameter reading exceeds or falls below the pre-defined threshold values, an alert mechanism is triggered. Integration with Twilio Service: Alerts are communicated to relevant stakeholders through the Twilio service, a cloud-based communication platform that facilitates the sending of SMS notifications to the designated recipients. These notifications serve as timely warnings, prompting stakeholders to take corrective actions to rectify the detected anomalies. The system generated descriptive statistics also helps to find sudden changes in parameters (outlier detection) that could indicate any kind of system failure and notify it by alert mechanism involved. They also set range to identify extreme that may harm the crops (a huge range in light intensity suggests improper lighting schedule). By coupling real-time monitoring with automated alerting mechanisms, the system empowers stakeholders to swiftly respond to environmental perturbations, thereby safeguarding crop health and ensuring the integrity of the farming operation. The system creates a timestamp snapshot for any kind of alert generated, to predict and handle future issues based on the deduced results from captured parameters. Automated Response: Upon receiving alert notifications, stakeholders are prompted to take immediate corrective actions to rectify the detected anomalies and restore optimal growing conditions within the vertical farming environment. Adjustment of Environmental Controls: Automated control systems governing parameters such as irrigation, lighting and ventilation may be activated to rectify deviations from optimal conditions. For instance, if the temperature exceeds the predefined threshold, the ventilation system may be activated to dissipate excess heat and restore thermal equilibrium. Safety Protocols Activation: In the event of critical alerts such as fire detection or security breaches, predefined safety protocols are activated to mitigate risks and ensure the safety of personnel and assets. This may entail triggering fire suppression systems, initiating evacuation procedures, or alerting emergency response teams. Physical Inspection: In instances where automated responses are insufficient or inconclusive, on-site personnel may conduct physical inspections of the farming facility to assess the nature and extent of the detected anomalies. These inspections serve as a supplementary measure to validate sensor readings and implement targeted interventions as necessary. Through the orchestration of these automated responses and human interventions, the system endeavors to maintain a harmonious equilibrium within the vertical farming environment, fostering optimal conditions for crop growth and ensuring the resilience and sustainability of the farming operation. The accuracy of the system is subject to factors like Sensor Calibration & Precision which is resolved or optimized to the fullest by the use of standard high precision optimal sensors (like SHT35, GS3, Apogee SQ-500, S8, Atlas scientific EZO-pH and other industry relevant sensing devices), a good actuator accuracy, data processing and filtering mechanisms that is resolved with the use of standard language modules and functionalities as they can remove anomalies and noise thus improving accuracy, application of machine learning models to predict optimal conditions and adjust control systems dynamically or to use redundancy (multiple sensors per parameter) to cross-verify readings. The response time is dependent on factors like sensor sampling rate which is usually faster for real-time processing and can be improved further with optimizing sensor polling rates, processing and decision time which can be improved with incorporation of AI-based predictions so that the system can pre-emptively adjust conditions before major fluctuations observed, actuator activation time. The system currently involves the conceptual use of EDGE Computing architecture where the server is locally installed or is on premise. To reduce the major errors, the system focuses on False positives (when system incorrectly activates actuators), False negatives (when system fails to take a specific action). The sensor drift errors can also be minimized by opting industry relevant long life span sensors. Periodic sensor calibrations are done to correct drift. The system also has in-built module code in python that runs error checking algorithms like Kalmann filter to reduce noise in the real-time collected data. Reinforcement learning models in future can be implemented or studied upon for further improvement in errors. Computational efficiency is subject to the use of hardware processing power, the complexity of the algorithms involved in analytics, data generation process or communication/data transfer process. Further, it is improved by the implementing the EDGE Model. In summation, the methodology outlined above encompasses a holistic and systematic approach to sustainable automated vertical farming, encompassing sensor deployment, data collection and analysis, alert generation and automated response mechanisms. There is an automatic generation and sending of reports to the major stakeholders periodically such as 1 DAY/ 1 WEEK/ 1 MONTH/ 3 MONTHS/ 6 MONTHS/ 1 YEAR based on customization. By synergistically integrating cutting-edge technologies with agronomic principles, the system empowers stakeholders to optimize resource utilization, enhance crop productivity and foster environmental stewardship in the pursuit of a more sustainable future for agriculture. 4 Implementation Hardware Setup Fig. 2 and 3 are describing the hardware setup that includes DHT-11, gas sensor, precipitation sensor, vibration sensor, tilt sensor, photosensor, soil moisture sensor, PIR motion detection sensor and fire detection sensor that are connected to the Arduino Uno Microcontroller for sensing different parameters involved. This Arduino code defines all the constants or the pin numbers associated with the sensors, include major libraries used and reads all the sensor data of digital and analog values in the required data types. Arduino Code Data collection from port to csv This Arduino code prints all the values that are collected in a comma separated values for easy storing of data in a CSV File. Real-time data on Serial The image is of the serial monitor on which the data is sensed from the various sensors are printed in comma separated values, from the serial monitor the data is read and converted into a CSV file. Fig. 4 is the results of the process or feature that involves calculating the descriptive statistics of the various sensor data that has been collected, it involves calculating the mean data, median data, mode data, standard deviation values and variance values for easy data analytics or later use or can be used for visualization. These data are generated automatically after a certain time which is custom to the user and they are stored in a separate CSV file which is created if not already present by the system. At the last, a summary file has been created that holds all the summary statistics of all the fields of data and for each column it generates various summary parameters. They involve count, mean, sd, min, max, 1st 2nd (Median) and 3rd Quartile of the data. Fig. 5 is the scatter plot that represents correlational analysis between two independent parameters like Temperature and Light Intensity, Humidity and light intensity. The vertical farming setup of sensors extends the control and alert system that involves a threshold value being set up by the user or each field or parameter value collected and if the sensor sends data of value more than the threshold value, it is modified to the respective department head via SMS. It involves the use of a third party API called Twilio which helps in SMS and other notification services which is shown in Fig. 6 The setup also facilitates the use of various pre-built software for accurate data analytics, and one such service is KNIME, where data file or CSV file can be imported and various operations on them can be done. Fig. 7 is a node setup of the basic analytics that is done with the data file being made. Fig. 8 and 9 represent the two categories of the data present and sorting of them for data analytics and they are categorized as binary and non-binary data. We use column filter node to filter out these columns. Binary data involves values which are of bool type and they are motion detected, fire state, rack stability, vibration state. Rest non-binary data includes temperature, humidity, light intensity, air quality, soil moisture and rain drop value or precipitation. Fig. 10 images are the descriptive statistics summary of the data generated using KNIME Software. The parameters here involve, name of the field, type, missing or NULL values, min, max, 1% 25%, 50%, 99%, 75% quartile, mean, sum, variance, standard deviation, skewness, kurtosis and 10 most common values. Fig. 11 (a-e) are showing scatter plots between various data fields that have been collected by the sensors and stored in the CSV file as columns. They represent how the various fields are correlated or show correlational relationships between them. This includes both binary and non-binary data. The different plot results provide insights into relationships between different contributing parameters which can help to assess the overall effectiveness of the system. The light intensity is remained stable for specific temperature ranges maintained around ~25°C, (Fig. 5) therefore no fluctuations observed. The system is maintaining stable light levels with optimal temperature for plant growth. The presence of data clusters suggests that the lighting system is automated and adjusts according to temperature variations. This reduces unnecessary energy consumption, consistency in plant growth and minimizing overheat, reducing system failures and excessive water loss. The system also reduces cooling requirements thus, saving more energy and reducing the costs. The humidity levels increase (~85-90%) with light intensity decrease (Fig. 5). The lowered adjustments of light prevents excessive plant transpiration and thus, reducing water loss. Risks of mold growth is reduced by maintaining optimal humidity-light balance with efficient energy usage by adjusting lighting according to humidity conditions. The plant stress is reduced, leading to faster growth cycles and higher yields. The strong positive correlation (Fig. 11a) suggests that better air quality is associated with increased precipitation which indicates improved air quality than open field, optimal CO 2 absorption for photosynthesis and regulated fungal growth, thus improving crop yield. This also implies energy-efficient air circulation reduces power usage. There is less manual ventilation and misting leads to lower labor costs, also reduces water wastage. The clusters of data points (Fig. 11c), indicate that at certain categorical soil moisture levels, precipitation is more frequent, therefore the system here define discrete irrigation schedules, ensuring plants receive water only when needed, reducing water waste and conserving electrical energy to turn sprinklers therefore, water conservation is evident from the correlation between soil moisture and precipitation achieving water efficiency and reduction in operational cost. The system is also able to prevent root rot, soil degradation and pollution. Humidity and soil moisture are directly correlated, but the overall variations are well-controlled (Fig. 11d). In general soil moisture levels often correspond with high humidity ensuring adequate hydration of plants, preventing excessive moisture and reducing fungal growth. This states that automated dehumidifiers prevent excessive moisture accumulation, reducing disease risks, improving climate stability within the farm and preventing mold and disease growth, leading to higher yield and crop stability. Temperature fluctuations do not cause major humidity spikes (Fig. 11e), however indicating positive correlation at high values of both the parameters implying automated climate regulation by HVAC systems and maintaining a stable microclimate thus, avoiding plant stress. The plants grow uniformly, leading to predictable and higher yields. The light intensity is 502.782 with a range of 10 - 1023, has high variance of 148,124.12 and standard deviation of 384.869. The data implies that the lighting system is highly adaptable and can adjust over a big range of values, based on the requirement at any instant as per the other parameters or factors. Thus, it saves or conserves electricity, minimizes energy waste and further reduces costs compared to traditional methods that rely on constant lighting or natural sunlight. The mean temperature value is 26.52°C with very low variance of 7.52 and standard deviation of 2.742 that indicates a stable climate-controlled environment. Stability in temperature further reduces energy costs for heating/cooling, leading to lower operational expenses and energy conservation. The soil moisture shows a moderate but controlled spread of water levels with mean of 43.45 (0 - 100 scale), variance 488.06 and standard deviation of 22.092. This indicates that the system works on with controlled irrigation based on sensor feedback ensures precise water delivery, reducing water wastage compared to conventional methods. The precipitation is 404.15 mean (0 - 1023 scale), with moderate skewness 0.14 proving a well-distributed watering system, possibly through automated hydroponic or aeroponic approach, which consumes 90% less water than existing methods. This reduces water wastage and contributes to water conservation. The mean humidity is 77.40% with moderate variance of 174.92 and standard deviation of 13.226 indicating a well-maintained growth environment. Plants experience fewer stress conditions, leading to higher and consistent yield. This also avoid excessive transpiration loss in plantations leading to less energy drain. Efficient humidity control prevents diseases like mold and fungi and thereby reducing reliance on pesticides. Negative skewness in temperature -4.84 here means most readings are close to the upper limit (around 26-27°C), showing a controlled environment. Light intensity have near-zero skewness, indicating balanced and uniform resource distribution. Minimal kurtosis fluctuations mean no extreme deviations, ensuring sustainable and reliable farming outputs. The implementation of the data analytics for Sustainable and Automated Vertical Farming system yields multifaceted outcomes, fundamentally transforming the management of vertical farming environments. By seamlessly integrating sensor data collection, threshold-based actuation and alerting mechanisms, the system ensures the maintenance of optimal environmental conditions conductive to plant growth and productivity. Enhanced safety protocols, facilitated by fire detection sensors and motion sensors, mitigate risks and safeguard personnel and assets within the farming setup. Moreover, the system promotes resource efficiency through judicious management of water, energy, and other inputs, while tailored environmental management allows for customized adjustments based on crop-specific requirements and operational constraints. Proactive intervention and mitigation, enabled by real-time alerting mechanisms, empower stakeholders to swiftly respond to detected anomalies, thereby ensuring operational continuity and resilience in the face of environmental perturbations. Together, these outcomes underscore the system's efficacy in enhancing sustainability, productivity and safety within the vertical farming domain. Conclusion The research "Sustainable Automated Vertical Farming" is an excellent example of how agriculture is being modernized by incorporating cutting-edge technologies. Through the use of Arduino for real-time data collecting and process automation, this work successfully tackles a number of important modern-day agricultural concerns, such as resource optimization, crop yield improvement, labor reduction and scalability for urban farming. By constantly monitoring environmental factors and making adjustments in real time, this technology guarantees the best possible use of resources, minimizing waste and improving sustainability. Furthermore, real-time monitoring and data analytics greatly increases crop yields through precise control. This technology has a wide range of uses, from research and smart greenhouses to commercial farming and urban agriculture. Large-scale commercial farms can maximize their productivity and streamline their operations, while urban agriculture benefits from sustainable food sources found within cities. In addition to improving efficiency and sustainability, this initiative provides a scalable response to the rising demands for food production in a world that is becoming more and more urbanized. This initiative serves as a trailblazing example for farming's future by offering a wide range of applications and addressing important agricultural concerns. The ethical implications of the study can involve job losses or workforce displacement as automation reduces needs for manual labor, small scale farmers may struggle to adapt or face unemployment. Economic inequality and access to technology as high-tech automated farms require significant one-time capital investment, which can in turn favor large corporations over small farmers. This can also increase economic equality where wealthy agri-businesses can control food production. Loss of traditional knowledge and cultural impact, where small scale family run farms may struggle to survive, remove cultural diversity in farming techniques. Ethical responsibility on food production, where the focus can mainly be on efficiency and profit, potentially compromising biodiversity and crop variety. Also, heavy reliance on AI-driven food production could create ethical concerns about food security if automation fails. Over-standardization of crops might lead to less resilient food systems in the face of climate change. The potential solutions for ethical concerns can include re-skill programs for farm workers in automated farming techniques, new job roles to be open in robotics maintenance, AI monitoring, and smart farm operations, providing government grants and subsidies for small farmers to adopt smart farming, implementing hybrid systems where AI supports traditional methods rather than replacing them, also encouraging community-led automation adoption rather than corporate-driven farming, put regulations to ensure AI-driven farming prioritizes biodiversity and food security. As a future scope, different correlation analysis helps to set threshold range for actuators to work upon, it also can help in developing traditional Machine Learning predicting models (Linear Regression, Lasso, Ridge, Elastic Net, SVM, Decision Trees, KNN, Naive Bayes, Random Forest) for maintenance, finding ambient conditions and prediction/deducing threats or alerts, therefore again setting threshold ranges for actuators to work on. (If a correlation shows that high light intensity increases temperature, the system can activate cooling systems to maintain optimal conditions). Declarations Author Contribution Prateek Barve, Mridulay Dixit have worked on hardware and data generation and Sritama Roy have completed drafting the manuscript. All authors reviewed the manuscript. Data Availability "The datasets generated and/or analysed during the current study are available in the data_verticalfarming repository, https://github.com/Prateek-Barve/data_verticalfarming.git" References M. 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Ros, “LED Lighting in Vertical Farming Systems Enhances Bioactive Compounds and Productivity of Vegetables Crops †,” pp. 1–8, 2022. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 May, 2025 Reviews received at journal 27 Apr, 2025 Reviews received at journal 17 Apr, 2025 Reviewers agreed at journal 05 Apr, 2025 Reviewers agreed at journal 02 Apr, 2025 Reviewers agreed at journal 02 Apr, 2025 Reviewers invited by journal 02 Apr, 2025 Submission checks completed at journal 21 Mar, 2025 First submitted to journal 18 Mar, 2025 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. 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03:32:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4948307/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4948307/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79833713,"identity":"7ec53048-9cb7-48fd-8b43-ead0ff8abf64","added_by":"auto","created_at":"2025-04-03 11:01:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75191,"visible":true,"origin":"","legend":"\u003cp\u003eBlock diagram representation of the proposed system\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4948307/v1/d1532ac6345f6fe932ebce89.png"},{"id":79833710,"identity":"95eaade7-41d4-4312-931b-83e06026da58","added_by":"auto","created_at":"2025-04-03 11:01:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":377263,"visible":true,"origin":"","legend":"\u003cp\u003eProposed System Hardware Setup Prototype\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4948307/v1/6c591a1475964381663ebe8b.png"},{"id":79834523,"identity":"a1396910-6f0f-4870-8ff1-90dad53849b2","added_by":"auto","created_at":"2025-04-03 11:09:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":324313,"visible":true,"origin":"","legend":"\u003cp\u003eImplementation of Hardware Setup\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4948307/v1/58fb99a00cb51340d70c5982.png"},{"id":79833709,"identity":"88103578-a233-468f-9c46-8d1db56c32dd","added_by":"auto","created_at":"2025-04-03 11:01:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":617732,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive statistics and summary obtained\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4948307/v1/8e396c0540f034838a4b886f.png"},{"id":79833712,"identity":"64a44f19-fd65-41aa-8d77-3aaef64b9e55","added_by":"auto","created_at":"2025-04-03 11:01:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":61600,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelational Analysis using Scatter Plot\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4948307/v1/7f182f23c12a23746dbdb8a6.png"},{"id":79833008,"identity":"b05481f5-9bc2-4b26-9f79-37bcf58f67e8","added_by":"auto","created_at":"2025-04-03 10:53:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":76380,"visible":true,"origin":"","legend":"\u003cp\u003eSMS Notifications Alert using Twilio API\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4948307/v1/8341063b995930fc78bbe2af.png"},{"id":79833732,"identity":"25cc8e1e-93d4-429c-bced-9231c3ae0344","added_by":"auto","created_at":"2025-04-03 11:01:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":36201,"visible":true,"origin":"","legend":"\u003cp\u003eNode Setup for Data Analytics Using KNIME\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4948307/v1/937b578ca805b269a9b4ff90.png"},{"id":79832998,"identity":"28615d40-0f8c-4fde-8a58-b4dec9b92428","added_by":"auto","created_at":"2025-04-03 10:53:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":31295,"visible":true,"origin":"","legend":"\u003cp\u003eFiltering Columns for non-binary data\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4948307/v1/f01e8a39fffde71c52952ec4.png"},{"id":79832999,"identity":"0c64c9de-b686-4636-acc6-e66ad22f46de","added_by":"auto","created_at":"2025-04-03 10:53:46","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":30703,"visible":true,"origin":"","legend":"\u003cp\u003eFiltering Columns for binary data\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4948307/v1/27da4a8de9dce76c7c024e42.png"},{"id":79833007,"identity":"c06ef171-ca49-4ca8-932f-d6e3c96f11a3","added_by":"auto","created_at":"2025-04-03 10:53:46","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":173219,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive Statistics using KNIME\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4948307/v1/20356aad0c1288bdd4bfef31.png"},{"id":79833001,"identity":"b7fcf76f-e5ad-4b9a-876b-ec564402a062","added_by":"auto","created_at":"2025-04-03 10:53:46","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":110070,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Scatter Plot for Non-binary data (Air Quality vs Precipitation) (b) Scatter Plot for Non-binary data (Temperature vs Light Intensity)\u003c/p\u003e\n\u003cp\u003e(c) Scatter Plot for Non-binary data (Soil Moisture Vs Precipitation (Raindrop) (d) Scatter Plot for Non-binary data (Soil Moisture Vs Humidity)\u003c/p\u003e\n\u003cp\u003e(e) Scatter Plot for Non-binary data (Temperature vs Humidity)\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-4948307/v1/2e29ee7b50d67e94bf301478.png"},{"id":79835593,"identity":"b299ae65-39f9-4781-a518-148eb91ce0e1","added_by":"auto","created_at":"2025-04-03 11:17:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2941512,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4948307/v1/e8c1fdce-5360-4089-a648-92fefef3bf7e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sustainable Automated Vertical Farming","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe dawn of the 21st century brings with it a myriad of challenges to traditional agricultural practices. Rapid population growth, urbanization, and climate change are placing unprecedented strains on human ability to feed the world's population while maintaining environmental sustainability [1]. In this context, the concept of vertical farming emerges as a beacon of hope, offering a revolutionary approach to agricultural production that is both efficient and environmental friendly [2]. Because traditional agriculture depends on large areas of land, it is becoming less and less viable in the face of urbanization and dwindling arable land [3]. Moreover, conventional farming methods often entail significant water usage, chemical inputs, and transportation costs, contributing to environmental degradation and greenhouse gas emissions [4]. It is critical to investigate alternate methods of producing food that can both meet the growing demand and lessen the impact on the environment as the world's population keeps growing [5]. The concept of agriculture itself is being challenged by the disruptive innovation known as vertical farming. At its core, crops are grown in vertically stacked layers in vertical farming, usually in controlled interior spaces like skyscrapers or warehouses [6]. The concept of Vertical Farming itself differentiates the thing from other traditional methods of farming by:\u003c/p\u003e\n\u003cp\u003ea) Improving Space Efficiency (higher yield in less space) - The vertically stacked farms can produce 15 to 100 times more number of plantations per square foot [6].\u003c/p\u003e\n\u003cp\u003eb) It allows us to implement urban agriculture where farms can be set up in cities and reducing the reliance on distant farmlands for easy and quick delivery, thus reducing prices due to cost cutting in transportation.\u003c/p\u003e\n\u003cp\u003eCompared to conventional farming techniques, vertical farms can achieve larger crop yields with less water and land use by utilizing cutting-edge technologies like Light Emitting Diode (LED) lighting, aeroponics, and hydroponics. This paradigm shift holds the promise of transforming the way human being produces food, making it more sustainable, resilient, and accessible to urban populations. The motivation behind the vertical farming extends beyond achieving more efficiency and productivity gains. It embodies a fundamental shift in relationship with food and the environment, emphasizing principles of sustainability, resilience, and self-sufficiency. Vertical farming lowers the need for long-distance transportation by bringing food production closer to urban areas, which lowers carbon emissions and decreases food waste [7], [8]. Moreover, as vertical farms are weather-independent, they can run all year round, providing a steady and dependable supply of food even in the face of disruptions brought on by climate change. Moreover, vertical farming presents an opportunity to tackle urgent social and economic issues, especially in densely populated urban areas. By creating local employment opportunities and revitalizing vacant urban spaces, vertical farming contributes towards the community development and economic growth [9]. Additionally, vertical farms can serve as educational hubs, raising awareness about the importance of healthy eating, environmental supervising, and sustainable agriculture [10].\u003c/p\u003e\n\u003cp\u003eFew challenges in traditional methods are:\u003c/p\u003e\n\u003cp\u003ea) High operational cost that involves unnecessary high energy consumption by lighting systems (tend to work even if not required), sprinklers, humidifiers and the dust suppression systems installed.\u003c/p\u003e\n\u003cp\u003eb) Recurring manual labor costs to operate the watering systems for plants, handling temperature and humidity control systems manually. There is also a significant challenge to find a suitable skilled labor as per the requirement.\u003c/p\u003e\n\u003cp\u003ec) No real time measure of crop growing parameters like temperature, humidity, air quality, light intensity, moisture present in the soil, pest detection, fire detection, rack stability.\u003c/p\u003e\n\u003cp\u003ed) No measured usage of natural resources like water, light, pesticides and fertilizers making the crop growing process quiet unsustainable.\u003c/p\u003e\n\u003cp\u003ee) No analytics and automation performed to allow system to be automated and make data driven decisions thereby making it more reliable.\u003c/p\u003e\n\u003cp\u003ef) No relevant solutions against seasonal constraints such as flooding, drought.\u003c/p\u003e\n\u003cp\u003eg) No proper/efficient resource allocation for optimized crop production.\u003c/p\u003e\n\u003cp\u003eAll these challenges imply lack of real time monitoring, automation, analytic and optimized decision making.\u003c/p\u003e\n\u003cp\u003eTechnical barriers such as high upfront costs, energy requirements, and complex logistic considerations pose significant obstacles to widespread adoption [11]. Furthermore, concerns about the long-term viability, profitability, and scalability of vertical farming operations are still unresolved. With the potential to completely transform agricultural methods, vertical farming offers a special chance to leverage data analytics and cutting-edge technology to improve sustainability and streamline operations. By integrating cutting-edge sensor networks, Internet of Things (IoT) devices, vertical farms can unlock a wealth of insights and capabilities that can significantly improve their efficiency, productivity, and environmental impact.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrecision Monitoring and Control:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen it comes to precise monitoring and controlling of vertical farming environments, data analytics can be extremely important. By placing a network of sensors throughout the growth chambers, critical parameters including temperature, humidity, light intensity, nutrient levels, and air quality can all be continuously monitored real-time. The abundance of data can be used to find the best growing environments, spot irregularities, and proactively modify environmental parameters to increase crop yields and quality. The study allows crop production to be climate independent allowing crops to be grown 24/7 throughout the year with no concerns of seasonal constraints like drought, flooding or temperature fluctuations. The study approaches waste minimization by detecting early stage of threat to plantations like of rodents, pests, fires and is acted upon instantly unlike other vertical farming approaches where the delay in response leads to 10-15% of crop wastage, following automated nutrient recycling that reduces fertilizer waste up to 50%. Further, automated harvesting techniques can reduce up to 20% crop wastage [12].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAutomated Irrigation and Nutrient Management:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe major improvement follows on significant reduction in wastage and usage of water resource by implementing practices like Hydroponic, aeroponic and aquaponic systems of water re-cycling and prevents issues or soil erosion (prevents top soil depletion). The study approaches water usage optimization by implementing precision irrigation like Hydroponics \u0026amp; Aeroponics that uses 95% less water than traditional farming. (embedded in the sprinklers). Real-time moisture monitoring also ensures proper utilization of water resources and prevent over-watering [13].\u003c/p\u003e\n\u003cp\u003eReal-time monitoring can further enhance the aspect by enabling automated and intelligent irrigation and nutrient management systems. Sensor data can be used to precisely monitor moisture levels, nutrient concentrations, and plant growth rates, allowing for the development of adaptive algorithms that optimize water and nutrient delivery based on real-time plant requirements. This approach not only conserves resources but also ensures optimal plant health and yield [14]. The automation helps in improved resource allocation that can help in optimizing lighting, irrigation and nutrients which is based on statistical insights. (if data shows that light intensity peaks at mid-day, the system automatically can reduce LED usage during that time).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnergy Optimization:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArtificial lighting and climate control systems are integral to vertical farming operations and can greatly increase energy consumption and costs. The implemented system is independent of source type of energy or power (on-grid/off-grid solar, regular supply). Irrespective of the source, in general it aims to reduce the power consumption (a part of the power source) by the use automation for resource allocation and environment monitoring control that reduces recurring labor costs associated. It involves the use of standard sensor and actuator modules that brings down the one-time capital expenditure. The study approaches energy consumption by following practices like, IoT controlled actuation or control system. For example, conventional vertical farms use fixed-intensity LEDs, leading to wastage of energy which is not the case here (dynamic lighting). The study uses automatic spectrum tuning and smart dimming reducing energy use by 30-50%, use of smart industry level sensor nodes with optimized Heating, Ventilation, and Air Conditioning (HVAC) cycles instead of running the sensors constantly. The system can also use integrated solar panels and battery storage for energy stability.\u003c/p\u003e\n\u003cp\u003eData analytics can play a crucial role in optimizing energy usage by analyzing historical data, identifying energy-intensive processes, and implementing predictive algorithms to anticipate and adjust energy usage based on factors such as crop growth stages, weather patterns, and occupancy levels [15].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e Pest and Disease Management:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVertical farms, despite their controlled environments, are not immune to pest infestations and plant diseases. Real-time monitoring and data analytics can aid in the early detection and prevention of these threats by analyzing sensor data, identifying patterns, and recognizing potential signs of infestation or disease before they become widespread [16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYield Prediction and Optimization:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA major objective of vertical farming is to increase crop yields with the least amount of resource inputs possible. Data analytics can play a pivotal role in achieving this by leveraging historical data, environmental conditions, and plant growth patterns to develop predictive models for crop yields. The system generates a correlational analysis report (and scatter plots) between various parameters to help to understand the relation between them which eventually help in balancing the parameters for ideal crop production. It helps to study how different parameter settings interact to maximize plant growth. For example, leafy greens like Spinach can thrive with humidity and moderate temperature (Data-driven adjustments). These models can then be used to optimize growing conditions, adjust nutrient levels, and implement strategies that consistently deliver high yields while minimizing waste and resource consumption. The real-time sensing and automation ensures stable food supply. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupply Chain Optimization:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to their urban locations, vertical farms have the potential to drastically lower the transportation expenses and carbon emissions related to conventional agriculture adding to a more sustainable approach. Further it can be enhanced by optimizing supply chain logistics, enabling real-time monitoring of crop growth and yield predictions, and facilitating efficient distribution and delivery to local markets and consumers [17].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSustainability Metrics and Reporting:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKey sustainability metrics must be measured and reported on since vertical farming seeks to be a sustainable and eco-friendly solution. The collection and analysis of data pertaining to waste generation, carbon footprint, and other environmental indicators, as well as resource consumption (water, energy, and nutrients) can be a crucial function of data analytics [15]. The system generates descriptive statistics like mean, median, mode, standard deviation, variance, quartiles (1%, 25%, 50%, 75%, 99%), min, max and summary of various parameters involved. It helps to determine ambient conditions for the crop production (e.g., irrigation can be controlled based on soil moisture summary), prevent skewed analysis of data collected in real time (rectify sudden temperature hikes), identify common conditions experienced by the crops (better ventilation control if constant high humidity), measures fluctuation in environmental conditions (high stress in plant if always high temperature or humidity) for better climate regulation. This information can be used to generate comprehensive sustainability reports, identify areas of improvement, and demonstrate the environmental benefits of vertical farming to stakeholders and consumers.\u003c/p\u003e\n\u003cp\u003eThe study focuses primarily on optimizing and improving efficiencies of the processes involved in production of crops or plantations in precision/vertical farming. The analytics part involves concepts like correlational analysis and descriptive statistics performed on the real-time collected data by sensors, for better and automatic decision making by the actuation system. The alert system helps in close monitoring of the actual system that is implemented with detailed report generation at a specific time interval subject to the consumer. The study also focuses on reducing operational cost of vertical farming, reducing excessive usage of natural resources and energy conservation, therefore contributing to create a sustainable environment. By leveraging sensor technology and automation, the study aims to develop a real-time monitoring and alert system capable of optimizing the growing conditions, mitigating risks, and maximizing crop yields in vertical farming environments. Through interdisciplinary research and collaboration, endeavour to unlock the full potential of vertical farming as a sustainable solution to the challenges of the 21st century.\u003c/p\u003e"},{"header":"2 Literature review","content":"\u003cp\u003eThe rapid growth of urban population and the mounting pressures of climate change have necessitated a paradigm shift in agricultural practices. In light of these difficulties, conventional horizontal farming practices\u0026mdash;which depend on large areas of arable land and substantial water resources\u0026mdash;become less and less viable. This requirement has led to a growing body of research on vertical farming, a novel strategy that re-imagines food production inside carefully regulated indoor spaces. Vertical farming holds the promise of achieving higher crop yields with minimal land and water usage, while dramatically reducing transportation costs and associated carbon emissions. However, realizing the full potential of this disruptive technology requires a multidisciplinary effort, drawing upon advancements in areas such as precision agriculture, data analytics, IoT, artificial intelligence (AI), and sustainable energy systems [14]. The literature review synthesizes insights from a diverse array of studies, highlighting the theoretical underpinnings, technological enablers, cultivation techniques, and environmental considerations that underpin the development of sustainable automated vertical farming systems. By integrating these varied perspectives, researchers and practitioners alike can unlock the transformative potential of vertical farming as a resilient and resource-efficient solution to address the global food security and environmental challenges of the 21st century.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTheoretical Foundations and Overviews:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBeacham, A.M., Vickers, L.H., \u0026amp; Monaghan, J.M. (2019) have provided a comprehensive summary of approaches towards vertical farming, including hydroponic, aeroponic, and aquaponic systems. The discussion was on various structural designs such as stacked layers, rotating systems, modular units, highlight the importance of environmental control and energy efficiency. Despommier, M. et.al., (2011) [13] have introduced the concept of \u0026quot;vertical farm\u0026quot; and outlines its potential benefits, such as year-round crop production, reduced transportation costs and efficient use of land and water resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrecision Agriculture and Data Analytics:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePierce, F.J. \u0026amp; Nowak, P. (1999) [14] have explored the principles of precision agriculture, emphasizing the use of GPS, remote sensing and variable rate technology to optimize inputs and maximize yields. The potential of site-specific crop management strategies enabled by data analytics was the point of discussion. Gebbers, R. \u0026amp; Adamchuk, V.I. (2010) [18] have described the use of proximal soil sensing and data fusion techniques to improve spatial resolution and accuracy of soil property maps enabling more precise decision-making in precision agriculture.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIoT and Sensor Networks:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMuangprathub, J. et. al., (2019) [19] have demonstrated the application of IoT technologies and data analytics in monitoring environmental factors including soil moisture, humidity and temperature in vertical farming setups. The proposed idea was a real-time monitoring system that can optimize growing conditions and reduce resource consumption. Ghobakhlou et. al., (2013) [20] have investigated the integration of wireless sensor networks (WSNs) in agricultural environments, showcasing their effectiveness in real-time monitoring of soil moisture, temperature and humidity, which are crucial for vertical farming operations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMachine Learning and AI:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDhanya et. al., (2021) [21] have provided insights into the use of machine learning methods in agricultural data analysis such as conventional neural network and artificial neural networks. The research group have showcased the potential of these techniques in predictive modeling and deep learning algorithms were for optimizing crop yield, pest management and resource allocation in vertical farming operations. Talaviya et. al., (2020) [22] have investigated the role of artificial intelligence (AI) in agriculture, emphasizing its potential in optimizing resource allocation through precision farming, crop management through predictive analytics and yield prediction through machine learning models tailored for vertical farming contexts.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEnergy and Lighting Optimization:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGonzalez et. al., (2021) [23] have shaded light on the importance of sustainable lighting technologies such as LED, in reducing energy consumption and optimizing plant growth in vertical farming environments. The performance of different LED configurations and their impact on crop yield and quality has huge impact. Kozai, T. et al. (2006) [24] explored energy-saving techniques for environmentally friendly plant factories such as optimizing air conditioning, dehumidification systems, implementing thermal insulation and integrating renewable energy sources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental Control and Optimization:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWang et. al., (2024) [25] have explored the optimization of environmental parameters in vertical farming through advanced control algorithms such as model predictive control (MPC) and fuzzy logic control. The demonstration was based on the potential of these techniques in maintaining optimal growing conditions while minimizing energy consumption and resource waste. Farhangi et. al., (2023) [26] have elucidated the trade-offs between various environmental parameters (e.g., temperature, humidity, light intensity) and crop yield, providing insights into the development of multi- objective optimization strategies for sustainable vertical farming.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCultivation Techniques and Systems:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAli AlShrouf (2017) [27] have demonstrated the potential of soil-less farming methods such as hydroponics and aeroponics, in conserving water, maximizing space utilization and enhancing crop productivity in vertical farming environments. The research group have compared the performance of different hydroponic systems and nutrient delivery methods. Bambhaniya et. al., (2023) [28] have investigated the applications of aquaponics in vertical farming, combining fish and plant cultivation in a closed-loop system for efficient resource cycling and nutrient management.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSustainability and Resilience:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAng et.al., (2022) [29] have highlighted the importance of sustainable energy solutions such as renewable energy integration (e.g., solar, wind), in reducing carbon footprint and enhancing energy resilience in agricultural operations, including vertical farming and finally have evaluated the feasibility and economic viability of different renewable energy sources for vertical farming facilities. Zheng et. al., (2024) [30] have explored climate-smart agriculture strategies for vertical farming, providing insights into resilient farming practices in the face of climate change uncertainties. The proposed adaptive management strategies such as drought-tolerant crop varieties and water-efficient irrigation systems were used to mitigate the impacts of climate change on vertical farming operations.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eMicrobiome and Nutrient Management:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVimal et al. (2024) [31] have elucidated the role of microbial communities in nutrient cycling, disease suppression, plant growth promotion and underscoring the importance of microbiome management for sustainable crop production in vertical farming and have investigated the effects of different cultivation techniques as well as environmental conditions on the microbial diversity and composition in vertical farming systems. Fathidarehnijeh et. al., (2024) [32] have looked into hydroponic system nutrient solution management techniques, providing insightful information on the way to maximize nutrient delivery and reduce waste in vertical farming operations. It was assessed how various nutrient formulations and replenishment techniques affected crop yield and quality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupply Chain and Blockchain:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuangjie et al. (2023) [33] have investigated the application of blockchain technology in agricultural supply chains, emphasizing its potential in enhancing transparency, traceability and trustworthiness in vertical farming operations. The proposed system was a blockchain-based traceability system that can track the provenance of crops, monitor environmental conditions and ensure food safety. Munim et.al., (2024) [34] have explored the concept of \u0026quot;blockchain-enabled Internet of Things (BIoT)\u0026quot; and its applications in smart agriculture, including vertical farming. In vertical farming ecosystems, the potential of blockchain technology has been benefited to enable safe data sharing, automate transactions and streamline supply chain management.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSocial and Economic Aspects:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBenis et. al., (2016) [35] have analyzed the economic viability and environmental impacts of urban vertical farming providing insights into the feasibility and scalability of the emerging agricultural model. A life cycle assessment and cost-benefit analysis were to evaluate the sustainability and profitability of different vertical farming scenarios. Reducing losses and waste, cutting transportation distances and accounting for technological advancements could be all important ways to improve the food supply chain\u0026apos;s efficiency and potentially mitigate its effects. Verma et.al., (2024) [36] have explored the social and economic dimensions of vertical farming, highlighting its potential in addressing food security, job creation and community development in urban areas. The challenges and opportunities associated with integrating vertical farming into urban planning and policy frameworks was discussed. The review highlights the multidisciplinary nature of sustainable automated vertical farming, encompassing diverse fields such as agriculture, engineering, data science, environmental science and economics. By integrating insights from these varied domains, researchers and practitioners can unlock the full potential of vertical farming as a sustainable and resilient solution to address the global food security and environmental challenges of the 21st century.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1: A systematic literature review of the recent agricultural approaches.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"456\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSr. No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTitle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePublication\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSummary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReferences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eVertical farming: a summary of approaches to growing skywards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eBeacham\u003c/p\u003e\n \u003cp\u003e, A.M.,\u003c/p\u003e\n \u003cp\u003eVickers,\u0026nbsp;L.H., \u0026amp;\u003c/p\u003e\n \u003cp\u003eMonagh\u0026nbsp;an, J.M.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eIncreasing crop yield per square metre of land is the aim of vertical farming (VF) techniques. Nonetheless, a variety of endeavours have been characterized by this word, ranging from massive skyscrapers utilized for the industrial cultivation of an extensive array of crops to small-scale or communal vegetable and herb gardening. \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e[37]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eThe Rise of Vertical Farms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eDickson Despommier\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eIntroduces\u0026nbsp;the\u0026nbsp;concept\u0026nbsp;of\u0026nbsp;\u0026quot;vertical\u0026nbsp;farm\u0026quot;\u0026nbsp;and\u0026nbsp;its potential benefits.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e[38]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAspects of Precision Agriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ePierce,\u003c/p\u003e\n \u003cp\u003eF.J. \u0026amp;\u003c/p\u003e\n \u003cp\u003eNowak,\u0026nbsp;P.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eExplores\u0026nbsp;precision\u0026nbsp;agriculture\u0026nbsp;principles\u0026nbsp;and\u0026nbsp;data-driven decision-making.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e[39]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePrecision Agriculture and Food Security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eGebbers,\u003c/p\u003e\n \u003cp\u003eR. \u0026amp; Adamch\u0026nbsp;uk, V.I.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eExamines\u0026nbsp;proximal\u0026nbsp;soil\u0026nbsp;sensing\u0026nbsp;and\u0026nbsp;data\u0026nbsp;fusion techniques. With precision agriculture, one can control the amount and quality of agricultural products produced while keeping an eye on the food production chain.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e[40]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eIoT-enabled system for monitoring and controlling vertical\u003c/p\u003e\n \u003cp\u003efarming operations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eHarn Tung Ng, Zhi Kean Tham, Nurul Amani Abdul Rahim, Ammar Wafiq Rohim,\u003c/p\u003e\n \u003cp\u003eWei Wen Looi, Nur Syazreen Ahmad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eThe proposed system can offer several benefits such as real-time environmental condition monitoring and control, reduced energy consumption, increased scalability, flexibility, support for sustainable and effective food production.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e[41]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eA decision support framework for the design and operation of sustainable urban farming systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eLanyu Li,\u003c/p\u003e\n \u003cp\u003eXian Li,\u003c/p\u003e\n \u003cp\u003eClive Chong,\u003c/p\u003e\n \u003cp\u003eChi-Hwa Wang,\u003c/p\u003e\n \u003cp\u003eXiaonan Wang\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eThe suggested framework is built on a comprehensive system model that takes into account the material and energy consumption during the production of vegetables as well as the environmental and economic outcomes of urban farming systems.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e[42]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePulsed LED‐Lighting as an Alternative Energy Savings Tech‐\u003c/p\u003e\n \u003cp\u003enique for Vertical Farms and Plant Factories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eErnesto Olvera‐Gonzalez, Nivia Escalante‐Garcia, Deland Myers, Peter Ampim, Eric Obeng, Daniel\u003c/p\u003e\n \u003cp\u003eAlaniz‐Lumbreras, and Victor Casta\u0026ntilde;o\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eEnergy conservation in systems is proposed for closed plants for production system (CPPS). \u0026nbsp;Vertical farms to improve utilisation efficiency and save costs without having a negative effect on crop or plant productivity.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e[23]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eA Comprehensive Review on Sustainable Industrial Vertical Farming\u003c/p\u003e\n \u003cp\u003eUsing Film Farming Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eZitian Zhang, Michel Rod, Farah Hosseinian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eVertical farming, or VF provides an innovative solution to various problems. VF uses stacks of growing racks and beds to maximise grow space per square foot of land. Water conservation is the usual goal of hydroponics.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e[43]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eVertical farming - smart urban agriculture for enhancing resilience and sustainability in food security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSoojin Oh et. al.,\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eIt talks about the way modern Internet of Things (IoT) applications have led to the development of sophisticated precision monitoring and controlling systems for vertical farming.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e[44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEnergy-efficient automated vertical farms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eMaxence Delorme, Alberto Santini\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eAutonomous vertical farms (VFs) are becoming more and more popular because they produce food with less water and pesticides than traditional agricultural methods because they yield much more per square metre. Three Mixed-Integer Linear Programming (MIP) formulations, along with a Constraint Programming model and valid inequalities, are proposed.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e[45]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eLED Lighting in Vertical Farming Systems Enhances Bioactive Compounds and Productivity of Vegetables Crops\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eCinthia N\u0026aacute;jera, Victor M. Gallegos-Cedillo, Margarita Ros and Jos\u0026eacute; Antonio Pascual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eThis study aimed to perform a bibliometric analysis on the benefits of vertical farming production systems on the nutraceutical quality parameters of horticultural crops. In addition, the main metrics that are employed to evaluate crop quality and yield are identified.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e[46]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eObservations from the Literature Review:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 is discussed about the literature highlights the immense potential of vertical farming to revolutionize agricultural practices, driven by the integration of cutting-edge technologies and data-driven approaches. Theoretical frameworks and overviews underscore the multifaceted benefits of vertical farming including efficient resource utilization, reduced environmental impact and year-round crop production. However, realizing these benefits hinges on the effective deployment of precision agriculture techniques, IoT sensor networks and advanced data analytics for real-time monitoring, predictive modelling and optimization of growth conditions. Machine learning and AI algorithms show promise in optimizing resource allocation, crop management and yield prediction while sustainable energy solutions such as LED lighting and renewable energy integration addressing the energy demands of controlled environments. Environmental control strategies and leveraging techniques like model predictive control and multi-objective optimization aim to balance resource efficiency with optimal plant growth. Innovative cultivation systems like hydroponics, aeroponics and aquaponics are coupled with microbiome management and nutrient solution strategies that offers pathways to sustainable soilless farming. Furthermore, blockchain technology and climate-smart agriculture practices enhance supply chain transparency, traceability and resilience to climate uncertainties. Economic viability assessments and explorations of social dimensions highlight the potential of vertical farming in addressing food security, job creation and community development, particularly in urban areas.\u003c/p\u003e"},{"header":"3 Proposed Methodology","content":"\u003cp\u003eIn the pursuit of sustainable automated vertical farming, a meticulous methodology is paramount to ensure the efficient collection, analysis and utilization of environmental data. This section outlines the sequential steps that are involved in putting the monitoring and alert system into place. It clarifies how different sensors are deployed, how data is collected, how analysis is done, and how alert mechanisms work.\u003c/p\u003e\n\u003cp\u003eThe block diagram of the proposed system is shown in Fig. 1. The system involves the use of 9 different types of sensors that include Precipitation sensor (for detecting rain water amount), soil moisture sensor, temperature and humidity sensor, air quality measurement sensor (or gas detection sensors), light intensity measurement sensor (Light Dependent Resistor), vibration sensor (to detect any unwanted movement, earthquakes or vibrations), tilt sensor (to ensure rack stability), fire detection sensor, PIR Sensor (passive infrared sensor for rodents or pest detection installed on adjacent edges of the entry point of rack or shelves). They collect the real time data observed in the crop environment and via serial communication devices like HC-05(wireless) connected to microcontrollers like Arduino, data is transferred to the main server or computer. \u0026nbsp; In the main server the python code converts serially received data format having different values to comma-separated values (CSV) format and store it in a folder. While data transferring the real-time values are standardized via standard scaler modules present and are checked for any threshold cross. If any deviation or anomaly occurs SMS Notification is sent via Twilio (3rd party service) Application Programming Interface (API) preinstalled which works on account generated serial ID, authorization token, a central call number and a list of recipient numbers.\u003c/p\u003e\n\u003cp\u003eThe generated data is sent to the analytics module for further processing to find or extract descriptive statics of the data including mean, median, mode, standard deviation, variance, quartile, summary, min, max. The data is also processed for correlational analysis and plots between all the necessary binary or non-binary data collected to understand the parameter dynamics and adjust it to balance the resources. Based on the statistics and relations, subject experts/matter can create suitable threshold in the system for appropriate decision making (data driven). A security code is also continuously run to detect potential threats by creating counts of the vital parameters like fire, quake, pest. For the specific timeline set by the consumer and sending (mail, SMS) reports to the concerned/registered person.\u003c/p\u003e\n\u003cp\u003eThe actuators involving sprinkler systems, lighting systems, dust suppression, humidifiers, ventilation system are then adjusted according to the systems requirement for crop production. The system also helps defining or analyzing stages of emergency with appropriate timestamps and possibly predict conditions to avoid them by also observing the parameters at that instant.\u003c/p\u003e\n\u003cp\u003eFor automation the system uses low powered serial Bluetooth communication like HC-05 compatible with any micro-controller like Arduino UNO which supports data transfers between the sensors a server and the server and actuators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensor Deployment and Data Collection:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe foundation of any data-driven system lies in the accurate and comprehensive collection of relevant environmental data. In the context of vertical farming, a multitude of sensors are deployed to monitor key parameters essential for optimal plant growth and environmental control.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Temperature Sensor: One of the fundamental parameters influencing plant metabolism and growth, the temperature sensor is strategically placed within the farming environment to capture ambient temperature variations.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Humidity Sensor: Ensuring adequate humidity levels is critical for preventing desiccation and maintaining plant health. The humidity sensor continuously measures the relative humidity within the vertical farming setup.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Motion Sensor: Beyond environmental parameters, security and intrusion detection are paramount in safeguarding the farming facility. Motion sensors are deployed to detect any unauthorized movement within the premises, triggering alerts if necessary.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Light Intensity Sensor: Light serves as the primary energy source for photosynthesis, making it imperative to monitor light intensity levels. The light intensity sensor quantifies the illumination within the farming environment, ensuring plants receive optimal light exposure.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Fire Detection Sensor: Safety is of paramount importance in any agricultural setting and fire detection sensors are employed to identify the presence of smoke or fire, triggering immediate alerts to prevent potential disasters.\u003c/p\u003e\n\u003cp\u003eAir Quality Sensor: Assessing air quality is crucial for maintaining a healthy and conducive environment for plant growth. Air quality sensors measure parameters such as carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) levels and volatile organic compounds (VOCs), providing insights into indoor air quality.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Soil Moisture Sensor: In hydroponic or soil-based cultivation systems, monitoring soil moisture levels is essential for ensuring proper hydration of plant roots. Soil moisture sensors gauge the moisture content in the growing medium, facilitating optimal irrigation management.\u003c/p\u003e\n\u003cp\u003eThe system is designed to be more scalable by adopting a modular design such as use of modular sensor nodes to expand monitoring, adopting a decentralize control system so that different levels of square foot areas or different plantations are operated independently, adopting EDGE Computing for local and real-time decisions. Apart from this, the system provides customization on different category and number of sensor nodes to the consumer as per the need or system requirement specifications. Scalability also depends on installation method involved and area of implementation or warehouse. The economic feasibility is associated with Initial Setup Costs, Energy consumption, labor cost, water and nutrient cost, Market Demand and Return on Investment (ROI).\u003c/p\u003e\n\u003cp\u003eThese sensors, meticulously deployed throughout the vertical farming setup, continuously collect real-time data on temperature, humidity, motion, light intensity, fire state, air quality, and soil moisture, forming the foundational dataset for subsequent analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Export to CSV:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe voluminous stream\u0026nbsp;of\u0026nbsp;sensor\u0026nbsp;data generated necessitates an\u0026nbsp;efficient\u0026nbsp;and structured approach for data management.\u0026nbsp;To\u0026nbsp;this\u0026nbsp;end,\u0026nbsp;the\u0026nbsp;collected\u0026nbsp;sensor\u0026nbsp;data\u0026nbsp;is processed\u0026nbsp;and\u0026nbsp;exported\u0026nbsp;to\u0026nbsp;CSV file format, a universally recognized format for tabular data storage.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Timestamping: Each sensor reading is meticulously timestamped to establish a chronological sequence of data points, facilitating temporal analysis and trend identification.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Data Organization: The exported CSV file is organized into distinct columns corresponding to each environmental parameter, ensuring data integrity and ease of analysis.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Storage Protocol: The CSV file, containing the aggregated sensor data, is stored in a designated repository or cloud storage platform, ensuring accessibility and data integrity for subsequent analysis and archival purposes.\u003c/p\u003e\n\u003cp\u003eThis structured approach to data exportation facilitates seamless integration with data analysis tools and ensures the availability of a comprehensive dataset for further exploration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u0026nbsp;Analysis\u0026nbsp;Using KNIME Software:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study uses Konstanz Information Miner (KNIME) platform for all kinds of data pre-possessing and analytics that is performed. It involves a No-Code workflow with easy to drag and drop interface, therefore minimizing the use of extensive programming and thus ideal for real-time data pipelines from sensors. It is easy to perform trend analysis, time series forecasting and correlational analysis on sensor data. It can be scheduled to run periodic reports for optimizing farm conditions. It supports seamless IoT and sensor data integration that connects to databases, IoT devices, and APIs for real-time farming system monitoring and it also supports CSV, JSON, SQL, and cloud storage formats. KNIME can collect real time data generated from the server for data cleaning, transformations and visualizations (being user friendly).\u003c/p\u003e\n\u003cp\u003eVarious other platforms can also be used or integrated with the system like Tableau, Power BI, Apache spark, Rapid Miner and is subject to the choice of the consumer or tool user.\u003c/p\u003e\n\u003cp\u003eWith the sensor data securely stored in CSV format, the next step entails in-depth analysis and exploration of the dataset to derive actionable insights and identify trends.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Descriptive Statistics: Utilizing KNIME\u0026apos;s robust statistical functionalities, descriptive statistics can be computed for each environmental parameter. These statistical measures provide a comprehensive overview of the dataset, elucidating central tendencies, variability and distribution characteristics.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Correlational Analysis: The interplay between various environmental parameters is investigated through correlational analysis. Specifically, correlations between temperature and light intensity, humidity and light intensity, and other relevant pairs of parameters are examined to identify potential relationships and dependencies.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Visualization Tools: KNIME\u0026apos;s rich suite of visualization tools is leveraged to generate insightful graphical representations of the sensor data. Line plots, scatter plots, histograms and heatmaps are employed to visually depict trends, patterns, and anomalies within the dataset, enhancing interpret-ability and facilitating data-driven decision-making.\u003c/p\u003e\n\u003cp\u003eThe categorization of the real-time collected data is done mainly on binary (fire detected, rack stability, vibration detection, motion detection) for alert reports and mechanisms to act on and non-binary data (moisture, temperature, humidity, precipitation, light intensity, air quality) for correlational analysis or descriptive statistic or future optimal conditions predictions, predictive maintenance models to determine relations for a balanced threshold environmental conditions of the crops.\u003c/p\u003e\n\u003cp\u003eThrough the iterative application of these analytical techniques, a clear understanding of the vertical farming environment is attained, enabling stakeholders to discern patterns, anticipate trends and optimize farming practices accordingly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThreshold Monitoring and Alerting:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProactive monitoring of environmental parameters is imperative to preemptively identify deviations from optimal conditions and mitigate potential risks to crop health and safety. To this end, predefined threshold values are established for each environmental parameter based on agronomic best practices and safety standards.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Threshold Definition: Threshold values are determined for parameters such as temperature, humidity, light intensity and soil moisture, delineating upper and lower bounds beyond which abnormal conditions are deemed to occur.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Alert Triggering Mechanism: The sensor data is continuously monitored in real-time and if any parameter reading exceeds or falls below the pre-defined threshold values, an alert mechanism is triggered.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Integration with Twilio Service: Alerts are communicated to relevant stakeholders through the Twilio service, a cloud-based communication platform that facilitates the sending of SMS notifications to the designated recipients. These notifications serve as timely warnings, prompting stakeholders to take corrective actions to rectify the detected anomalies.\u003c/p\u003e\n\u003cp\u003eThe system generated descriptive statistics also helps to find sudden changes in parameters (outlier detection) that could indicate any kind of system failure and notify it by alert mechanism involved. They also set range to identify extreme that may harm the crops (a huge range in light intensity suggests improper lighting schedule). By coupling real-time monitoring with automated alerting mechanisms, the system empowers stakeholders to swiftly respond to environmental perturbations, thereby safeguarding crop health and ensuring the integrity of the farming operation. The system creates a timestamp snapshot for any kind of alert generated, to predict and handle future issues based on the deduced results from captured parameters. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAutomated Response:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon receiving alert notifications, stakeholders are prompted to take immediate corrective actions to rectify the detected anomalies and restore optimal growing conditions within the vertical farming environment.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Adjustment of Environmental Controls: Automated control systems governing parameters such as irrigation, lighting and ventilation may be activated to rectify deviations from optimal conditions. For instance, if the temperature exceeds the predefined threshold, the ventilation system may be activated to dissipate excess heat and restore thermal equilibrium.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Safety Protocols Activation: In the event of critical alerts such as fire detection or security breaches, predefined safety protocols are activated to mitigate risks and ensure the safety of personnel and assets. This may entail triggering fire suppression systems, initiating evacuation procedures, or alerting emergency response teams.\u003c/p\u003e\n\u003cp\u003ePhysical Inspection: In instances where automated responses are insufficient or inconclusive, on-site personnel may conduct physical inspections of the farming facility to assess the nature and extent of the detected anomalies. These inspections serve as a supplementary measure to validate sensor readings and implement targeted interventions as necessary.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Through the orchestration of these automated responses and human interventions, the system endeavors to maintain a harmonious equilibrium within the vertical farming environment, fostering optimal conditions for crop growth and ensuring the resilience and sustainability of the farming operation.\u003c/p\u003e\n\u003cp\u003eThe accuracy of the system is subject to factors like Sensor Calibration \u0026amp; Precision which is resolved or optimized to the fullest by the use of standard high precision optimal sensors (like SHT35, GS3, Apogee SQ-500, S8, Atlas scientific EZO-pH and other industry relevant sensing devices), a good actuator accuracy, data processing and filtering mechanisms that is resolved with the use of standard language modules and functionalities as they can remove anomalies and noise thus improving accuracy, application of machine learning models to predict optimal conditions and adjust control systems dynamically or to use redundancy (multiple sensors per parameter) to cross-verify readings.\u003c/p\u003e\n\u003cp\u003eThe response time is dependent on factors like sensor sampling rate which is usually faster for real-time processing and can be improved further with optimizing sensor polling rates, processing and decision time which can be improved with incorporation of AI-based predictions so that the system can pre-emptively adjust conditions before major fluctuations observed, actuator activation time. The system currently involves the conceptual use of EDGE Computing architecture where the server is locally installed or is on premise.\u003c/p\u003e\n\u003cp\u003eTo reduce the major errors, the system focuses on False positives (when system incorrectly activates actuators), False negatives (when system fails to take a specific action). The sensor drift errors can also be minimized by opting industry relevant long life span sensors. Periodic sensor calibrations are done to correct drift. The system also has in-built module code in python that runs error checking algorithms like Kalmann filter to reduce noise in the real-time collected data. Reinforcement learning models in future can be implemented or studied upon for further improvement in errors.\u003c/p\u003e\n\u003cp\u003eComputational efficiency is subject to the use of hardware processing power, the complexity of the algorithms involved in analytics, data generation process or communication/data transfer process. Further, it is improved by the implementing the EDGE Model.\u003c/p\u003e\n\u003cp\u003eIn summation, the methodology outlined above encompasses a holistic and systematic approach to sustainable automated vertical farming, encompassing sensor deployment, data collection and analysis, alert generation and automated response mechanisms. There is an automatic generation and sending of reports to the major stakeholders periodically such as 1 DAY/ 1 WEEK/ 1 MONTH/ 3 MONTHS/ 6 MONTHS/ 1 YEAR based on customization. By synergistically integrating cutting-edge technologies with agronomic principles, the system empowers stakeholders to optimize resource utilization, enhance crop productivity and foster environmental stewardship in the pursuit of a more sustainable future for agriculture.\u0026nbsp;\u003c/p\u003e"},{"header":"4 Implementation","content":"\u003cp\u003eHardware Setup\u003c/p\u003e\n\u003cp\u003eFig. 2 and 3 are describing the hardware setup that includes DHT-11, gas sensor, precipitation sensor, vibration sensor, tilt sensor, photosensor, soil moisture sensor, PIR motion detection sensor and fire detection sensor that are connected to the Arduino Uno Microcontroller for sensing different parameters involved.\u003c/p\u003e\n\u003cp\u003eThis Arduino code defines all the constants or the pin numbers associated with the sensors, include major libraries used and reads all the sensor data of digital and analog values in the required data types.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArduino Code\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collection from port to csv\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eThis Arduino code prints all the values that are collected in a comma separated values for easy storing of data in a CSV File.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReal-time data on Serial\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe image is of the serial monitor on which the data is sensed from the various sensors are printed in comma separated values, from the serial monitor the data is read and converted into a CSV file.\u003c/p\u003e\n\u003cp\u003eFig. 4 is the results of the process or feature that involves calculating the descriptive statistics of the various sensor data that has been collected, it involves calculating the mean data, median data, mode data, standard deviation values and variance values for easy data analytics or later use or can be used for visualization. These data are generated automatically after a certain time which is custom to the user and they are stored in a separate CSV file which is created if not already present by the system.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the last, a summary file has been created that holds all the summary statistics of all the fields of data and for each column it generates various summary parameters. They involve count, mean, sd, min, max, 1st 2nd (Median) and 3rd Quartile of the data.\u003c/p\u003e\n\u003cp\u003eFig. 5 is the scatter plot that represents correlational analysis between two independent parameters like Temperature and Light Intensity, Humidity and light intensity.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The vertical farming setup of sensors extends the control and alert system that involves a threshold value being set up by the user or each field or parameter value collected and if the sensor sends data of value more than the threshold value, it is modified to the respective department head via SMS. It involves the use of a third party API called Twilio which helps in SMS and other notification services which is shown in Fig. 6\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The setup also facilitates the use of various pre-built software for accurate data analytics, and one such service is KNIME, where data file or CSV file can be imported and various operations on them can be done. Fig. 7 is a node setup of the basic analytics that is done with the data file being made.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFig. 8 and 9 represent the two categories of the data present and sorting of them for data analytics and they are categorized as binary and non-binary data. We use column filter node to filter out these columns. \u0026nbsp;Binary data involves values which are of bool type and they are motion detected, fire state, rack stability, vibration state. Rest non-binary data includes temperature, humidity, light intensity, air quality, soil moisture and rain drop value or precipitation.\u003c/p\u003e\n\u003cp\u003eFig. 10 images are the descriptive statistics summary of the data generated using KNIME Software. The parameters here involve, name of the field, type, missing or NULL values, min, max, 1% 25%, 50%, 99%, 75% quartile, mean, sum, variance, standard deviation, skewness, kurtosis and 10 most common values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFig. 11 (a-e) are showing scatter plots between various data fields that have been collected by the sensors and stored in the CSV file as columns. They represent how the various fields are correlated or show correlational relationships between them. This includes both binary and non-binary data.\u003c/p\u003e\n\u003cp\u003eThe different plot results provide insights into relationships between different contributing parameters which can help to assess the overall effectiveness of the system. The light intensity is remained stable for specific temperature ranges maintained around ~25\u0026deg;C, (Fig. 5) therefore no fluctuations observed. The system is maintaining stable light levels with optimal temperature for plant growth. The presence of data clusters suggests that the lighting system is automated and adjusts according to temperature variations. This reduces unnecessary energy consumption, consistency in plant growth and minimizing \u0026nbsp; \u0026nbsp; \u0026nbsp;overheat, reducing system failures and excessive water loss. The system also reduces cooling requirements thus, saving more energy and reducing the costs. The humidity levels increase (~85-90%) with light intensity decrease (Fig. 5). \u0026nbsp;The lowered adjustments of light prevents excessive plant transpiration and thus, reducing water loss. Risks of mold growth is reduced by maintaining optimal humidity-light \u0026nbsp; balance with efficient energy usage by adjusting lighting according to humidity conditions. The plant stress is reduced, leading to faster growth cycles and higher yields. The strong positive correlation (Fig. 11a) suggests that better air quality is associated with increased precipitation which indicates improved air quality than open field, optimal CO\u003csub\u003e2\u003c/sub\u003e absorption for photosynthesis and regulated fungal growth, thus improving crop yield. This also implies energy-efficient air circulation reduces power usage. There is less manual ventilation and misting leads to lower labor costs, also reduces water wastage. The clusters of data points (Fig. 11c), indicate that at certain categorical soil moisture levels, precipitation is more frequent, therefore the system here define discrete irrigation schedules, ensuring plants receive water only when needed, reducing water waste and conserving electrical energy to turn sprinklers therefore, water conservation is evident from the correlation between soil moisture and precipitation achieving water efficiency and reduction in operational cost. The system is also able to prevent root rot, \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; soil degradation and pollution. Humidity and soil moisture are directly correlated, but the overall variations are well-controlled (Fig. 11d). In general soil moisture levels often correspond with high humidity ensuring adequate hydration of plants, preventing excessive moisture and reducing fungal growth. This states that automated dehumidifiers prevent excessive moisture accumulation, reducing disease risks, improving climate stability within the farm and preventing mold and disease growth, leading to higher yield and crop stability. Temperature fluctuations do not cause major humidity spikes (Fig. 11e), however indicating positive correlation at high values of both the parameters implying automated climate regulation by HVAC systems and maintaining a stable microclimate thus, avoiding plant stress. The plants grow uniformly, leading to predictable and higher yields. The light intensity is 502.782 with a range of 10 - 1023, has high variance of 148,124.12 and standard deviation of 384.869. The data implies that the lighting system is highly adaptable and can adjust over a big range of values, based on the requirement at any instant as per the other parameters or factors. Thus, it saves or conserves electricity, minimizes energy waste and further reduces costs compared to traditional methods that rely on constant lighting or natural sunlight. The mean temperature value is 26.52\u0026deg;C with very low variance of 7.52 and standard deviation of 2.742 that indicates a stable climate-controlled environment. Stability in temperature further reduces energy costs for heating/cooling, leading to lower operational expenses and energy conservation. The soil moisture shows a moderate but controlled spread of water levels with mean of 43.45 (0 - 100 scale), variance 488.06 and standard deviation of 22.092. This indicates that the system works on with controlled irrigation based on sensor feedback ensures precise water delivery, reducing water wastage compared to conventional methods. The precipitation is 404.15 mean (0 - 1023 scale), with moderate skewness 0.14 proving a well-distributed watering system, possibly through automated hydroponic or aeroponic approach, which consumes 90% less water than existing methods. This reduces water wastage and contributes to water conservation. The mean humidity is 77.40% with moderate variance of 174.92 and standard deviation of 13.226 indicating a well-maintained growth environment. Plants experience fewer stress conditions, leading to higher and consistent yield. This also avoid excessive transpiration loss in plantations leading to less energy drain. Efficient humidity control prevents diseases like mold and fungi and thereby reducing reliance on pesticides. Negative skewness in temperature -4.84 here means most readings are close to the upper limit (around 26-27\u0026deg;C), showing a controlled environment. Light intensity have near-zero skewness, indicating balanced and uniform resource distribution. Minimal kurtosis fluctuations mean no extreme deviations, ensuring sustainable and reliable farming outputs.\u003c/p\u003e\n\u003cp\u003eThe implementation of the data analytics for Sustainable and Automated Vertical Farming system yields multifaceted outcomes, fundamentally transforming the management of vertical farming environments. By seamlessly integrating sensor data collection, threshold-based actuation and alerting mechanisms, the system ensures the maintenance of optimal environmental conditions conductive to plant growth and productivity. Enhanced safety protocols, facilitated by fire detection sensors and motion sensors, mitigate risks and safeguard personnel and assets within the farming setup. Moreover, the system promotes resource efficiency through judicious management of water, energy, and other inputs, while tailored environmental management allows for customized adjustments based on crop-specific requirements and operational constraints. Proactive intervention and mitigation, enabled by real-time alerting mechanisms, empower stakeholders to swiftly respond to detected anomalies, thereby ensuring operational continuity and resilience in the face of environmental perturbations. Together, these outcomes underscore the system\u0026apos;s efficacy in enhancing sustainability, productivity and safety within the vertical farming domain.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe research \"Sustainable Automated Vertical Farming\" is an excellent example of how agriculture is being modernized by incorporating cutting-edge technologies. Through the use of Arduino for real-time data collecting and process automation, this work successfully tackles a number of important modern-day agricultural concerns, such as resource optimization, crop yield improvement, labor reduction and scalability for urban farming. By constantly monitoring environmental factors and making adjustments in real time, this technology guarantees the best possible use of resources, minimizing waste and improving sustainability. Furthermore, real-time\u0026nbsp;monitoring and data analytics greatly increases crop yields through precise control. This technology has a wide range of uses, from research and smart greenhouses to commercial farming and urban agriculture. Large-scale commercial farms can maximize their productivity and streamline their operations, while urban agriculture benefits from sustainable food sources found within cities. In addition to improving efficiency and sustainability, this initiative provides a scalable response to the rising demands for food production in a world that is becoming more and more urbanized. This initiative serves as a trailblazing example for farming's future by offering a wide range of applications and addressing important agricultural concerns.\u003c/p\u003e\n\u003cp\u003eThe ethical implications of the study can involve job losses or workforce displacement as automation reduces needs for manual labor, small scale farmers may struggle to adapt or face unemployment. Economic inequality and access to technology as high-tech automated farms require significant one-time capital investment, which can in turn favor large corporations over small farmers. This can also increase economic equality where wealthy agri-businesses can control food production. Loss of traditional knowledge and cultural impact, where small scale family run farms may struggle to survive, remove cultural diversity in farming techniques. Ethical responsibility on food production, where the focus can mainly be on efficiency and profit, potentially compromising biodiversity and crop variety. Also, heavy reliance on AI-driven food production could create ethical concerns about food security if automation fails. Over-standardization of crops might lead to less resilient food systems in the face of climate change. The potential solutions for ethical concerns can include re-skill programs for farm workers in automated farming techniques, new job roles to be open in robotics maintenance, AI monitoring, and smart farm operations, providing government grants and subsidies for small farmers to adopt smart farming, implementing hybrid systems where AI supports traditional methods rather than replacing them, also encouraging community-led automation adoption rather than corporate-driven farming, put regulations to ensure AI-driven farming prioritizes biodiversity and food security.\u003c/p\u003e\n\u003cp\u003eAs a future scope, different correlation analysis helps to set threshold range for actuators to work upon, it also can help in developing traditional Machine Learning predicting models (Linear Regression, Lasso, Ridge, Elastic Net, SVM, Decision Trees, KNN, Naive Bayes, Random Forest) for maintenance, finding ambient conditions and prediction/deducing threats or alerts, therefore again setting threshold ranges for actuators to work on. (If a correlation shows that high light intensity increases temperature, the system can activate cooling systems to maintain optimal conditions).\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePrateek Barve, Mridulay Dixit have worked on hardware and data generation and Sritama Roy have completed drafting the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003e\"The datasets generated and/or analysed during the current study are available in the data_verticalfarming repository, https://github.com/Prateek-Barve/data_verticalfarming.git\"\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eM. Barrett, M. Marino, F. Brkic, and C. A. 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Santini, \u0026ldquo;Energy-efficient automated vertical farms R,\u0026rdquo; \u003cem\u003eOmega\u003c/em\u003e, vol. 109, p. 102611, 2022, doi: 10.1016/j.omega.2022.102611.\u003c/li\u003e\n \u003cli\u003eN. Cinthia, V. M. Gallegos-cedillo, and M. Ros, \u0026ldquo;LED Lighting in Vertical Farming Systems Enhances Bioactive Compounds and Productivity of Vegetables Crops \u0026dagger;,\u0026rdquo; pp. 1\u0026ndash;8, 2022.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sustainable agriculture, Vertical farming, Sensor technology, Data analytics, Internet of Things (IoT), Hydroponics","lastPublishedDoi":"10.21203/rs.3.rs-4948307/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4948307/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study offers a thorough strategy for automated vertical farming that is sustainable in response to the urgent problems that traditional agricultural methods are currently facing. The primary objective is to design and implement a real-time monitoring and alert system which is capable of optimizing growing conditions and ensuring the long-term viability of vertical farming operations. The system architecture revolves around the integration of sensor technology, automation, and data analytics to enable proactive management of environmental parameters crucial for plant growth. Specifically, sensors are connected to a microcontroller to capture data on temperature, humidity, motion, light intensity, fire state, air quality, and soil moisture. This data is continuously monitored and analyzed in real-time to detect anomalies and deviations from optimal conditions. Upon detecting abnormal conditions such as low light intensity, fire risks, low soil moisture, or unstable rack conditions, the system triggers alerts via SMS using the Twilio service. These alerts are sent to some designated recipients, allowing for timely interventions to rectify the situation and prevent potential crop loss. The proposed system aims to address key gaps and areas of improvement in current vertical farming practices. The results or potential benefits involve reduction in energy costs and water usage based on analytic report custom to farmer, reducing operational expenses and trade it with one-time capital expense by means of reduction in labor costs cut by 40-60% (estimated) and further help to avoid skill issue concern of the labors. The crop production process is made more efficient and optimized with precise use of natural resources which in-turn makes the system more sustainable and improves crop yield and quality. The process gets automated by using actuators which rectify or act on emergency alerts instantly, therefore increasing reliability and propose customization to the customer. By providing a proactive approach to environmental management, it seeks to enhance resource efficiency, crop quality, and overall productivity. Moreover, by leveraging advanced data analytics techniques, the system offers insights into environmental trends and patterns, enabling informed decision-making and adaptive responses. Overall, the system represents a significant step towards the realization of sustainable automated vertical farming. By harnessing the power of sensor technology, automation, and data analytics, it offers a scalable and efficient solution to the challenges facing modern agriculture. It can fully realize vertical farming's potential as a resilient and sustainable food production system for the future use.\u003c/p\u003e","manuscriptTitle":"Sustainable Automated Vertical Farming","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 10:53:41","doi":"10.21203/rs.3.rs-4948307/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-06T07:56:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-27T05:53:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-17T08:39:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38027585702891334258310678177538655123","date":"2025-04-05T18:01:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325488595363275577256675864781615097947","date":"2025-04-02T20:06:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66602899875352642775991246062008845505","date":"2025-04-02T06:26:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-02T06:22:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-21T20:42:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-18T13:09:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"64b9e2ce-e9aa-4790-9aa2-1e535559c845","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":46599547,"name":"Physical sciences/Engineering/Electrical and electronic engineering"},{"id":46599548,"name":"Physical sciences/Energy science and technology"}],"tags":[],"updatedAt":"2025-08-08T07:24:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-03 10:53:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4948307","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4948307","identity":"rs-4948307","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}