Mapping the Evolution of Agriculture 4.0: A Bibliometric Analysis of Research Trends

Mapping the Evolution of Agriculture 4.0: A Bibliometric Analysis of Research Trends | 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 Research Article Mapping the Evolution of Agriculture 4.0: A Bibliometric Analysis of Research Trends Bikram Barman, Rashmi Singh, Rabindra Nath Padaria, Sk Wasaful Quader, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4948484/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract The term "agriculture 4.0" refers to integrating artificial intelligence, big data, cloud computing, the Internet of Things and advanced robotics into agriculture. The field of Agriculture 4.0 research has seen a surge in attention as sustainable agriculture has gained more prominence. This study concentrated on conducting a bibliometric analysis of Agriculture 4.0 and its growth. The Dimensions.ai data used in the study was produced using the search terms “Agriculture 4.0," "Smart Farming," "Farming 4.0," and "Digital Agriculture.” A comprehensive dataset consisting of 1,458 relevant documents has been identified, retrieved, and compiled into a CSV format for further analysis. The retrieved data was visualized and analyzed using suitable software. It was that the information and computing sciences field had the maximum number of publications on Agriculture 4.0 (1,015), followed by Agriculture, veterinary and food science (487). The majority of articles (1,074) addressed Sustainable Development Goal 2, which has hunger as its main focus. Based on co-authorship analysis, India, China, and the USA emerged as the leading nations both in impact and research volume, with other countries clustering around them. The University of Guelph, Wageningen University and Research and Anna University were the three organisations with respectively the most impact in terms of total citations. According to the sources' citation analyses, readers were more influenced by the "Computers and Electronics in Agriculture" publication when it came to Agriculture 4.0 research. The Agriculture 4.0 research involves many stakeholders; thus, a broad multidisciplinary approach is necessary. Hence, to solve the issue of Agriculture 4.0, multidisciplinary researchers ought to collaborate rather than act alone. Agricultural Engineering Agroecology Environmental Engineering Agriculture 4.0 Farming 4.0 Smart Farming Digital Agriculture bibliometrics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction Digitalization is an increasingly ubiquitous trend in the socio-technical process involved in implementing digital innovations (Klerkx et al., 2019 , Latzer, 2022 ). The process of digitalization affects society's conventional structure, institutions, and organisations in addition to bringing forth technological advancements (Bargh and Troxler, 2020 ). With the advancement of industry 4.0 technologies, digitalization is progressing daily. Cyber-physical systems are being implemented systematically under Industry 4.0. Information from every aspect of the production process is synchronised via computational cyberspace (Mosterman et al. , 2016). These technologies include sensors, artificial intelligence, blockchain, augmented reality, cloud computing, robotics, digital twins, robotics, additive manufacturing, robotics, system integration, and ubiquitous connectivity (Alm et al., 2016 ; Smith, 2018 ; Huang et al., 2021 ). The introduction of Industry 4.0 simplified daily tasks for people. It is predicted that global population growth is anticipated by 31% by 2050 which will increase the food production need (Baldos et al ., 2014; Raj et al., 2021 ; Huang et al., 2021 ) to provide food for over 9 billion people (Searchinger et al., 2014 ). This population growth will result in a 71% increase in resources needed over the next three decades (Ayaz et al., 2019 ). Climate change is another significant threat to agriculture, as it reduces the extension of arable land available and erodes productivity (Sott et al., 2020 ). Massive food waste is another sign of market inefficiency (Maffezzoli et al., 2022 ). To maximise agricultural productivity, disruptive strategies aimed at minimising waste and losses throughout the entire value chain must be incorporated into production systems (Leia et al., 2020 ; Vågsholm et al., 2020 ). We must make the switch from conventional farming methods to the cutting-edge strategies of Agriculture 4.0 to overcome these problems. This next phase is crucial for sustaining the world’s growing population. Agriculture 4.0 represents the application of Industry 4.0 principles to farming, highlighting the interdependence of industry and agriculture. Innovations in one sector directly impact the other, driving progress across both fields. The literature identifies several key phases in the significant evolution of agricultural systems around the world over time. The “Agriculture 1.0” also known as the “first agricultural revolution,” (Meliala et al., 2019 ), involved the transition from hunting and gathering to settled farming. In the 18th century the “second agricultural revolution” termed the British agricultural revolution (Simpson et al. , 2004) initiated and was marked by increased agricultural production due to mechanization and improved land productivity (Liu et al., 2021 ). The “third agricultural revolution,” often referred to as the Asian Green Revolution, occurred in the 1960s and brought advancements like hybrid seeds, irrigation systems, pest control, and synthetic fertilizers (Rose et al ., 2018). Most recently, we have entered into the Agriculture 4.0 era (Fuglie et al., 2020 ; Araújo et al., 2021 ), which is known as the “fourth agricultural revolution,” integrates advanced technologies into farming practices. Since the year 2002 when introduction of digitization was felt in the agriculture sector the industry has started integrating this new phenomenon. It introduces a new revolution of agriculture commonly referred to as Agriculture 4. 0 (Raj et al., 2021 ). Technological advancements are blending the process of agriculture making the sector a very relevant one. Defined broadly, “Digital agriculture” refers to the expansion of the IoT’s applications, big data analysis, cloud computing, AI, and highly developed robotics in the agricultural domain (Bollini et al., 2019 ; Bolfe et al., 2020; Abbasi et al., 2022 ). On the same note, smart farming also seeks to increase productivity and effectiveness by lessening the detrimental repercussions of farming activities on the environment (Duncan et al., 2021 ; Mukherjee et al., 2021 ). Thus, with the help of these technologies, Agriculture 4. 0 ensures the rational use of inputs like fuel, fertilizers, seeds, and herbicides (Raj et al., 2021 ). Nevertheless, the greatest difficulty in the development of digital agriculture is that of connecting all these mentioned technologies that appear across the different branches of sciences (Iglesias et al., 2020 ). This makes overcoming the integration issues a key to the advancement of Agriculture 4. 0. The advancement that we discover in the framework of digital agriculture also has drawbacks. Thus, to achieve a sustainable perspective for Agriculture 4. 0, one has to understand the effects of this concept as well as its advantages and disadvantages (Klerkx et al., 2019 ; Abbasi et al., 2022 ). To date, the use of information technologies in agriculture is one of the topical issues of scientific discussions (Muhuri et al., 2019 ; da Silveira et al., 2021 ). Such technologies have the capability of cantering for environmentally friendly measures in order to enhance food security. Recently, improving the method of the use of limited inputs in agricultural production has become the subject of intense interest among many scholars (Bertoglio et al., 2021 , Mukherjee et al., 2021 ). However, it is still necessary to note that in this process of shifting to new technologies, human activity is still paramount in making appropriate and vital decisions. Unfortunately, very often one can have an impression that farmers just watch this revolution happening (Bollini et al., 2019 ). The Industry 4.0 with its technology packages of can make unforeseen social, environmental, and economic impacts on agriculture (Arifin et al., 2022 ). Therefore, comprehending Agriculture 4.0 involves understanding its core themes, which centre around the integration of these technologies in realm of agriculture (Latino et al., 2021 ). Although there is no definition of digital agriculture with is universally accepted, research in this area has been growing rapidly (da Silveira et al., 2021 ). This study aims to explore and elucidate the central themes of Agriculture 4.0 through a thematic approach. Adopting emerging technologies is vital for the advancement of Agriculture 4.0 (Lioutas et al., 2022). However, the specific subjects covered within this field remain somewhat ambiguous. This study seeks to clarify these themes and provide a deeper thoughtful research paradigm in digital agriculture. Bibliometric analysis of Agriculture 4.0 to understand research trends, influential works, and key areas of focus in this emerging field. This analysis aids researchers in identifying pivotal studies, collaborations, and gaps in the literature. The outcome offers a comprehensive overview of current advancements, enabling informed future research directions. Materials and methods A thorough analysis of bibliometric data was done to thoroughly explore the domain within the framework of Agriculture 4.0. Groos and Pritchard (1969) coined the term "bibliometric analysis" to describe a set of quantitative methods for tracking and analysing the spread of literature on a particular topic (Roemer, 2015; Zulfah and Astuti, 2023). This quantitative method examines and evaluates bibliographic resources within a specific scientific field, including publications, citations, authors, and institutions (de Sousa, 2021; Miltos et al., 2023). A comprehensive and methodical assessment of various bibliographic components, including publication numbers, authors, journals, keywords, and citations, is part of the methodology. Scientists can uncover significant connections, patterns, and trends in Agriculture 4.0 by investigating these factors. Development of a Theme and Objective Before commencing bibliometric analysis within the realm of Agriculture 4.0, it's imperative to delineate clear research inquiries and objectives. Following an exhaustive review of literature, the study recognized these subsequent questions: a. What are the predominant study trajectories within the context of Agriculture 4.0? b. Who are the most productive authors within the agricultural sphere, particularly concerning Agriculture 4.0? c. What are the prevalent keywords utilized by authors in the domain of Agriculture 4.0? d. Which institutes wield significant influence in advancing Agriculture 4.0? e. How does research on Agriculture 4.0 diverge across different regions or nations? f. What are the commonly utilized Agriculture 4.0 technologies, and how have they evolved? g. Which publications receive the most citations in the realm of Agriculture 4.0 how do they shape the trajectory of the field? h. To what extent does Agriculture 4.0 research within the ambit of contributing to specific research categories and long-term objectives? These research queries are adaptable and subject to modification, analysis, and tailored to suit exclusive study interests and objectives. Subsequently, the next step entailed formulating a specific search strategy. Database selection and search queries Database selection and search query processes are crucial in information retrieval systems (Groos and Pritchard, 1969; Tobias et al., 2021). Effective database selection for keyword-based searches across multiple structured data sources involves summarizing keyword relationships in relational databases to choose the most suitable databases for a given query (Bei et al., 2007). The importance of selecting appropriate databases for systematic reviews emphasizes the need for a systematic approach to minimize bias and enhance the validity of search results. Key databases for Agriculture 4.0 research include Scopus, Web of Science, Dimensions, Lens, and Google Scholar. Dimensions database is advantageous as general access as compared to Scopus or Web of Science which incur costs. Thus, open-access data was used for the present study. Dimensions are commonly used for citation analysis, and research objectives are typically used to guide database selection (Aliche et al., 2020). The Dimensions database provides a huge amount of raw data, but the cleaning of data is difficult and time-consuming. We carefully selected search phrases like "Agriculture 4.0," "Smart Farming," "Farming 4.0," and "Digital Agriculture" to ensure papers were relevant to the study. Search and sieve the publications The search was then conducted to obtain articles that were further reviewed according to the quality and their relevance (Gusenbauer and Haddaway, 2020; Lefebvre et al., 2019). The subject was basically searched for English articles and only titles and abstracts of the research papers where consulted. No bias was carried out in data downloading, and all the relevant data were downloaded on the same day. 2,389 documents formatted in CSV were found during this search; the ones that were unwanted or contained incomplete articles were optionally eliminated. Another essential process in bibliometric analysis is the data cleansing process through which irrelevant or low-quality data is eliminated to enable high-quality relevant data for response to research questions most of the time (Andersen 2013, Linnenluecke, 2020). Data cleaning is essential in order to get proper and meaningful outcome even if it involves time and efforts (Osborne, 2012; Ganti and Sarma, 2013). Consequently, the available number of papers to perform bibliometric analysis was 1,458 only. Analyze the bibliographic data This study focused on bibliographic data which involved aspects such as authors, year of publication, titles of the journals and citation indexes (Meho and Yang, 2007). For this purpose, various bibliometric indices, like the number of publications, citation rates, and results from the frequent word analysis were used (Ellegaard and Wallin, 2015; Kalantari et al., 2017). Further, bibliometric analyses such as co-citation, co-authorship, and bibliographic coupling analyses were also employed (Song et al., 2023). For bibliometric analysis R programming as well as the Biblioshiny package (Aria & Cuccurullo, 2017) was used in this study. For the network analysis and visualisation, we employed one of the strongest tools known as VOSviewer which facilitated mapping of the relations between the publications and authors. Thus, with the help of this tool, the identified patterns and trends in the field were big in terms of Agriculture 4.0. Precisely, the analysis helped to recognize the most important authors, articles, and topics in the given citation pool. The approach used in the present research not only pays attention to the role of individual authors and works but also uncovers patterns and processes at the level of the scientific field. Therefore, the present research fosters a significant reference material for scholars and professionals who strive to address the challenges and embrace the opportunities of the dynamically transforming field of Agriculture 4. 0 effectively. Interpret the findings After the bibliometric data analysis, we briefly compared and contrasted our findings with the objectives set for the study. For Agriculture 4.0 key themes, important authors, and research collaborations that explain the current state of the field and specify the topics that require further study were identified. The scrutinized activity identified research collaborations and the lack of research on certain topics, which will determine further work on important themes. These findings are most useful to inform this study and determine areas that needed more researches (Mishra et al., 2016; Abdullah et al., 2023). The adoption of this method enhanced the knowledge of Agriculture 4.0 and new avenues of development in the specific field have been created (Pizzi et al., 2020). Results and discussion Trends in Publications This is another way through which we have comprehensively studied Agriculture 4. As for results obtained from analysis of the Dimensions data, there was no research indicated. This is a critical advancement that provides evidence of the ever-evolving interest and expansion of this discipline and the field of study in general; namely the level of publications. We monitored this development over the years with bibliometric analysis, which is a valid method to identify a growth or shrinkage of research topics (Wang et al., 2022 ). The examination of the number of publications per annum tells more about how Agriculture 4. 0 research is progressing. This information is quite useful in ascertaining which research area is gaining or losing popularity hence aiding in the direction of resources to the most rewarding and worthy research areas. Writing in 2019, Gautam considers bibliographic analysis as a method for evaluation of the research productivity and for identification of the cross-disciplinary impacts. That is why it is used in Agriculture. It is disturbing that to a large extent 0 research has yielded findings that may be useful for additional inquiries and in establishing blueprints for the deployment of resources. The Fig. 1 illustrates the publication trend over the duration of the research from the year 2015 to the year 2024. Based on the trend established above, annual individual publications are rising gradually, with the highest number recorded in 2023. Such a pattern indicates tremendous improvement in productivity of the area of concern. Citation analysis was done to evaluate the research's impact, the outcomes are shown in Fig. 2 . The graph unequivocally shows that there have been more citations annually, and that this increase has been happening at a faster rate over time. The increasing number of citations that the research has received indicates that the work done in the field has become more and more influential. As a result, Fig. 2 presents a more intricate perspective on the research impact. than Fig. 1 , which displays a productivity metric. Figure 2 shows that the field has produced significant research that has attracted the scholarly community's increasing attention and recognition. Research impact and quality are evaluated, as well as patterns and trends in scientific publications, using bibliometric analysis. The popularity and impact of scientific works are reflected in the number of citations that are received annually. It illustrates how study trends have changed over time. When assessing an academic's or research group's performance, bibliometric analysis often relies on the volume of publications as a primary metric. The quantity of publications is widely used to compare researchers or academic groups and is thought to be a useful indicator of research productivity. In this study, we focused on the number of published works across multiple scientific realms. The total amount of publications in each category is depicted in Fig. 3 . According to the data, information and computing sciences gain the maximum number of publications. The other large spheres are agriculture, veterinary sciences, and food science. It's important to recognize that the number of publications does not always correspond to the quality of research or its relevance. Meanwhile, the data that covers other bibliometric measures such as the impact factor of journals, trends of co-authorship, and citation accounts might show specific areas that make substantial progress in their respective spheres. As such, they provide a comprehensive assessment of research productivity and its impact. Therefore, a variety of indicators are necessary for a comprehensive bibliometric analysis. Furthermore, the research topic influences how relevant the quantity of publications is. As a result, while publications are an essential part of bibliometric analysis, adding more indicators results in a more accurate and thorough assessment of research performance. More specifically, further literature searches were performed on articles concerning the SDGs. The aim of the bibliometric analysis was to identify, assess, and review relevant research studies in order to determine achievements in implementing the seventeen Sustainable Development Goals that were endorsed by the UN in 2015. The bibliometric analysis of publications related to the SDGs helps identify the gaps in the research as well as new trends (Raman et al., 2024 ). It also presents the mapping and cooperation between the researchers and institutions involved in SDGs (Trane et al., 2023 ). The results are illustrated in Fig. 4 , which presents the results of the publications classification based on the themes of studies in order to determine the distribution of the research output across the thematic areas of SDGs. This paper assists in identifying the type of region that should be focused on for reaching these goals and gives valuable information regarding the research fields that are engaged in the advancement of the SDGs. The graph that displays the theme distribution of the publications regarding Agriculture 4.0, which is connected to SDG 2: As can be seen from Issues such as Zero Hunger and affordable and Clean Energy, amongst others, stresses how critical publication is in determining funding and policy as well as research initiatives purported to support the accomplishment of the SDGs. By leveraging advanced technologies and innovative practices, Agriculture 4.0 aims to enhance food security, improve agricultural productivity, and ensure sustainable energy use in agricultural processes. To assist policymakers and funders in noticing domains that should be researched in further detail to advance the progress, Fig. 4 has arranged the publications according to the major SDG categories. Based on this, in the SDG bibliometric analysis, publications are central to the former to generate evidence that can facilitate decisions needed in the attainment of development goals that are sustainable. Productive authors The most productive authors in the specified field were identified in accordance with the data obtained from the Dimensions database. The volume of citations received for their work was a measure of its significance. As for productivity, Table 1 shows ten authors involved in our annotated bibliography ordered by the number of citations, or frequency. The paper presents valuable comprehension of the leading scholars in the given area of study and their significant findings to knowledge escalation (Rita et al., 2020 ). The authors are presented in Table 1 in the decreasing order of citation index obtained from Google Scholar. This way, citations per publication helped assess an author’s genuine impact more effectively. The research calibre of the authors was defined further by the analysis where authors with more than 700 citation had cited their papers more frequently. The number of times a publication has been cited in other scholarly works, rather than just the raw citation totals, is vital as a bibliometric index in comparing the productivity of an author’s work and leads to greater precision in measuring the impact. Diagnostic tests; (Bornmann et al., 2008 ; Durieux and Gevenois, 2010 ). Candidates with higher citation per publication scores are likely to have a sizable impact in their discipline, in variance and policy. Table 1 Top 10 authors worldwide in terms of productivity. Authors No. of documents No. of citations Citation per publications Emma Jakku 8 1,182 147.75 Laurens W A Klerkx 15 2,001 133.40 Panagiotis G Sarigiannidis 10 947 94.70 Lei Shu 6 783 130.50 Ashok Kumar Das 6 482 80.33 James Alan Turner 7 440 62.86 Emily Duncan 7 382 54.57 Callum R Eastwood 7 380 54.29 Evan D G Fraser 9 322 35.78 Michael S Carolan 7 310 44.29 Authors’ production over time It is essential to evaluate an author's productivity and impact in order to determine their relevance in a given field. The top nine authors over the previous 12 years in terms of productivity are shown in Fig. 5 . The quantity of papers produced determines productivity, whereas annual citations indicate impact. The colour depth of a circle signifies the annual citation count, while the circle's size corresponds to the number of publications. Klerkx and Lee are the most productive, according to the graph, with Chen contributing consistently from 2012 to 2024. This bibliometric analysis does a good job of illustrating these researchers' productivity and influence. Journals Table 2 presents the findings of a citation analysis that was done to determine the impact of publishing sources. The most papers were published in the IOP Conference Series: Earth and Environmental Science, which was followed by Sensors and Computers and Electronics in Agriculture, according to a citation analysis. Agricultural Systems and Sensors was the second most cited topic overall, after Computers and Electronics in Agriculture. Though the impact of the sources varied, Agricultural Systems had the highest citations per paper (CPP), followed by Computers and Electronics in Agriculture and the Journal of Rural Studies. Table 2 Top 17 sources around the globe. Source Documents Citation Citation per paper IOP Conference Series Earth and Environmental Science 66 233 3.53 Sensors 57 1,909 33.49 Computers and Electronics in Agriculture 53 3,040 57.36 Sustainability 50 826 16.52 Agriculture 45 907 20.16 IEEE Access 38 907 20.16 Agronomy 30 1,128 37.60 International Journal for Research in Applied Science and Engineering Technology 30 8 0.27 Applied Sciences 25 373 14.92 E3S Web of Conferences 22 25 1.14 Agricultural Systems 21 2,966 141.24 Journal of Physics Conference Series 20 93 4.65 Journal of Rural Studies 18 687 38.17 Multimedia Tools and Applications 17 27 1.59 Procedia Computer Science 16 489 30.56 Precision Agriculture 16 471 29.44 Smart Agricultural Technology 15 217 14.47 Co-authorship analysis of authors Another method that is often used to establish how authors collaborate in the research society is co-authorship analysis (Alireza et al., 2021 ). This method provides analysis of the structural organisation of the community, in which the authors are located in the node while collaboration is represented at the edge of a network, according to Andrew ( 2023 ). Degree centrality, betweenness centrality, and clustering coefficients are applied to work with these networks (Silvia et al., 2023 ; Mitrović et al., 2023 ). While degree centrality is calculated as the number of direct connections made by an author, and betweenness centrality is used to highlight an author's function as a link between various subgroups, clustering coefficients, on the other hand, are used to identify closely-knit research groups. Thus, this research aims to provide significant knowledge of the social and intellectual structure of the field to determine authoritative writers, established research groups, and connections between them. Furthermore, the analysis will point out the new and developing research topics and directions. Degree centrality deals with self-co-authorship connections, betweenness centrality accounts for interconnection and clustering coefficients represent density and authors’ clustering. This analysis helps identify key authors, research clusters, and potential research gaps or collaborations. In Fig. 6 , a network of co-authors is shown, with each node representing an author, links indicating co-authorship, node size showing publications, and node colour indicating author country. Research groups that are closely related to one another are displayed in the network as node clusters with similar colours. This bibliometric analysis identifies authors with significant network influence, seen in their high degree and betweenness centrality. These authors bridge research groups, fostering collaboration and knowledge exchange, and driving Agriculture 4.0 research synergies. We identified 12 non-interconnected clusters but found three closely related groups. Co-authorship analysis also identifies collaboration types like intra-institutional, inter-institutional, international, and interdisciplinary offering insights into collaboration dynamics and influencing factors. This analysis, performed with VoSviewer, found 462 authors meeting the criteria, 39 related, and 8 clusters showing authors' collaborative intellectual spaces. Co-authorship analysis of countries To Bornmann and Wohlrabe ( 2019 ) the comparative analysis of the citation of publications of various national origin is made feasible by normalised citation values that are derived from networks of visualized maps created by VOSviewer. The every figure in these maps illustrates a country and the size of this figure indicates the importance and quality of the country’s production in terms of normalised citation value. According to the discussion, research output of a nation studied is scientifically valuable if the node size of the nation is larger. Therefore, it provides a significant measure which can explain how effective various nations are relative to the results of productivity and the impact of the study. We also carry out a country co-authorship analysis based on the same dataset, thus using fundamental criteria to clean up data and establish a solid identification of groups of collaborating countries with one another. To satisfactorily address all the sampled criterion’s facet, at least five documents were needed for each country; only 61 out of 100 countries complied with this aspect. The map is visualised in Fig. 7 , which displays eight groups of nations that have established strong cooperative ties. Labels and circles denote the various countries on the visualization map, along with circle size corresponding to the number of documents produced by each nation. Greater impact in particular studies is indicated by larger circles and labels. India stands out for its research output and impact, and other nations have clustered around it, demonstrating a strong collaboration network between them. The clustering and interconnectedness of the circles suggest robust international cooperation, with certain countries like India, the United States, and China serving as central hubs around which other nations cluster. Country Scientific Production The scientific output level in terms of research publications related to Agriculture 4.0 is indicated by the blue colour on the map of the country's scientific production. This visualization showcases the countries contributing significantly to the advancement of knowledge in this field. It's intriguing to see how nations with robust research output in Agriculture 4.0 engage in collaborative efforts, transcending geographical boundaries for mutual scientific benefit. For instance, India, China, the USA, and the United Kingdom have demonstrated notable scientific production in the field of Agriculture 4.0. These countries have engaged in significant research output, contributing substantially to the global body of knowledge. While such scientific production may not directly address the development theme, it can facilitate the exchange of policies and knowledge and stimulate market collaborations. Thus, advocating for international scientific production to bolster innovation and knowledge dissemination is paramount, as it fuels advancements and fosters development within the field. The nation with the most citations was ascertained by performing a citation analysis on all the countries. The results indicate that the USA has the greatest citations (285), followed by Australia, the UK, Korea, and New Zealand. Citation Analysis The primary applications of co-citations are the evaluation of scientists, publications, and scientific institutions (Rousseau and Zuccala, 2004 ). Citation analyses can be used to identify various disciplines and emerging specialities. In addition, they can determine the transdisciplinary or multidisciplinary nature of research programs and initiatives by examining journal relationships (Morillo, 2003). Citations are linked together through co-citations, which are analogous to word co-occurrence metrics (Boyack and Klavans, 2010 ). Using citations in a paper as a basis for developing links with other researchers. It is a piece of data that is more than just bibliographic data. A citation signifies the author's choice to highlight the connection between another author's work and their own within their text at a specific point. According to Shaw ( 1983 ), "citation develops a relation between authors, which measures the degree to which they interact indirectly". The visual map of the sources' and organisations' citation analyses is displayed in Figs. 10 and 11 , respectively. Citation Analysis of Organisation To better understand how organisations cite one another, a minimum of five papers from each organisation were set. Nine clusters and a total of 75 related organisations were identified based on the criteria. The universities with the greatest impact were Wageningen University and Research, International Hellenic University, University of Guelph, and Anna University in terms of total citations. This suggests that these universities play a significant role in the field, contributing substantially to the body of knowledge and influencing other researchers and institutions. Citation Analysis of Sources Citing a document in a reference list, according to Egghe and Rousseau ( 1990 ), signifies that the author believes there is a connection such as a similarity in topic, problem, or methodology of the referenced with cited materials. Citation analysis was emphasised as the field that investigates these relationships. It is essentially able to identify disciplines and newly emerging specialties, to determine whether research programmes and initiatives are transdisciplinary or multidisciplinary, by utilising journal relationships. Co-citation is similar to word co-occurrence similarity metrics, connects cited documents together (Boyack and Klavans, 2010 ). Document co-citation networks were developed to demonstrate the variety of documents and communities within the systems thinking literature (Trujillo and Long, 2018 ). The citation analysis was conducted using a criterion that required each source to have a minimum of five documents. 48 sources met this criterion, yielding 7 clusters. Each bubble's size indicates the influence of a particular source. Figure 11 makes clear which journals have received the most citations and publications within these clusters: Sensors, Sustainability, and Agronomy. Co-citation source analysis Co-citation has three main uses, according to Rousseau ( 1994 ) information retrieval and search; modelling historical progress of science and technology; and assessment of scientists, journals, and scientific organisations using both qualitative and quantitative methods. Essentially, citation analysis can identify disciplines and new fields of study, as well as whether research projects and programmes are transdisciplinary or multidisciplinary, by utilising journal relationships. Co-citation, which is comparable to word co-occurrence metrics in similarity analysis, connects cited documents. After the co-citation analysis of publication sources was completed, 298 out of 8149 sources met the threshold. Six clusters in all were identified by the analysis. Based on Fig. 12 , we can deduce that the journals Computer and Electronics in Agriculture, Agricultural Systems, NJAS Impact in Agricultural and Life Sciences, Remote Sensing, etc. had the greatest quantity of citations and publications among the clusters. Keyword Analysis Through keyword analysis, we can learn which words authors believe are crucial to their papers. Based on the terms selected by the researchers for the manuscript's title and abstract, we performed a keyword analysis for this analysis. The main cause of this is that we are unable to perform keyword analysis with the Dimensions data that we were able to retrieve. Using keyword analysis has a few benefits because it provides insight into popular research topics. The research direction within the study's theme will also be made clear by the analysis. Figure 13 displays the keyword analysis results. The criteria for this analysis required a minimum of 60 occurrences of a term. Out of 32,174 terms, 113 met this threshold. By calculating the relevance score using the default 60% of key terms, we selected 68 terms. Ultimately, 58 keywords were chosen and grouped into two clusters. The keyword analysis results clearly show that 'Smart Farming' is the most prominent term. Other frequently highlighted terms include 'Internet,' 'IoT,' 'Model,' 'Sensor,' 'Network,' 'Development,' and 'Digital Agriculture,' reflecting the current research trends in Agriculture 4.0 technologies and their applications. Conclusion The study provides a comprehensive overview of research papers on Agriculture 4.0 using bibliometric analysis. The analysis involved authorship, co-authorship among countries, and citation sources, revealing an increasing trend in publications peaking in 2023, alongside a moderate but growing citation rate indicating rising scholarly influence. Key research subjects include information and computing sciences, agriculture, veterinary, food sciences, and engineering, reflecting a strong focus on digital integration and improved agricultural practices. Additionally, Agriculture 4.0 research aligns with Sustainable Development Goals (SDGs) like SDG 2 (Zero Hunger) and SDG 7 (Affordable and Clean Energy), stressing its relevance in addressing global sustainability challenges. Prominent keywords such as "Smart Farming," "IoT," and "Digital Agriculture" illustrate current research trends. Top authors, including Emma Jakku and Laurens W.A. Klerkx, have made significant contributions, as evidenced by high citation counts and impactful publications. Key journals, such as "Computers and Electronics in Agriculture" and "Sensors," are pivotal in disseminating influential research. This bibliometric analysis offers valuable insights for guiding future research, resource allocation, and policy-making in Agriculture 4.0. It is crucial to promote these technologies in developing countries to increase agricultural output and improve efficiency. Developments in engineering technology will further support the development of automated and controlled agricultural systems. Although the scope of the search results was limited, this study paves the way for future research, necessitating more large-scale and impactful studies. Policymakers and researchers are encouraged to develop further studies and collaborate with other organizations to help achieve SDG goals. Declarations Availability of data and materials The datasets produced and analyzed in this study are accessible within this manuscript. All unique findings from this study are detailed in the article and supplementary materials. For more information, please reach out to the corresponding authors. Funding sources This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. 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citations.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-4948484/v2/b160f7b236edb114a000ec7a.png"},{"id":72919949,"identity":"21f20b4c-a9de-4893-8799-10ae8d65d32f","added_by":"auto","created_at":"2025-01-03 16:46:38","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":575883,"visible":true,"origin":"","legend":"\u003cp\u003eCitation analysis visualisation map (organisation)\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-4948484/v2/6e6f41e3cb1a97c01d61cf7d.png"},{"id":72919964,"identity":"5acc8e6d-c447-4fc4-9be1-8d4d71589f3b","added_by":"auto","created_at":"2025-01-03 16:46:39","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":595369,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization map of the citation analysis (sources).\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-4948484/v2/3dc9106a4551d658b003db3b.png"},{"id":72919931,"identity":"754a31aa-ef90-49b5-886b-001dfa538842","added_by":"auto","created_at":"2025-01-03 16:46:37","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":625525,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization map of the co-citation analysis (sources).\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-4948484/v2/a730811135dd68c4af89e4c8.png"},{"id":72920664,"identity":"946cb6e8-e351-410a-ab86-7287b44e4ebf","added_by":"auto","created_at":"2025-01-03 16:54:38","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":1676365,"visible":true,"origin":"","legend":"\u003cp\u003eThe network visualization map featuring two clusters of keywords\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-4948484/v2/805b5cf0660e881b8ba5a318.png"},{"id":72921634,"identity":"60bcac1b-4729-4a88-b743-26908d5ce3a1","added_by":"auto","created_at":"2025-01-03 17:10:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5305237,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4948484/v2/b0bfd005-a7b1-4031-994e-4ea78b423997.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMapping the Evolution of Agriculture 4.0: A Bibliometric Analysis of Research Trends\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDigitalization is an increasingly ubiquitous trend in the socio-technical process involved in implementing digital innovations (Klerkx et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Latzer, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The process of digitalization affects society's conventional structure, institutions, and organisations in addition to bringing forth technological advancements (Bargh and Troxler, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). With the advancement of industry 4.0 technologies, digitalization is progressing daily. Cyber-physical systems are being implemented systematically under Industry 4.0. Information from every aspect of the production process is synchronised via computational cyberspace (Mosterman \u003cem\u003eet al.\u003c/em\u003e, 2016). These technologies include sensors, artificial intelligence, blockchain, augmented reality, cloud computing, robotics, digital twins, robotics, additive manufacturing, robotics, system integration, and ubiquitous connectivity (Alm et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Smith, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The introduction of Industry 4.0 simplified daily tasks for people. It is predicted that global population growth is anticipated by 31% by 2050 which will increase the food production need (Baldos \u003cem\u003eet al\u003c/em\u003e., 2014; Raj et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to provide food for over 9\u0026nbsp;billion people (Searchinger et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This population growth will result in a 71% increase in resources needed over the next three decades (Ayaz et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Climate change is another significant threat to agriculture, as it reduces the extension of arable land available and erodes productivity (Sott et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Massive food waste is another sign of market inefficiency (Maffezzoli et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To maximise agricultural productivity, disruptive strategies aimed at minimising waste and losses throughout the entire value chain must be incorporated into production systems (Leia et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; V\u0026aring;gsholm et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe must make the switch from conventional farming methods to the cutting-edge strategies of Agriculture 4.0 to overcome these problems. This next phase is crucial for sustaining the world\u0026rsquo;s growing population. Agriculture 4.0 represents the application of Industry 4.0 principles to farming, highlighting the interdependence of industry and agriculture. Innovations in one sector directly impact the other, driving progress across both fields. The literature identifies several key phases in the significant evolution of agricultural systems around the world over time. The \u0026ldquo;Agriculture 1.0\u0026rdquo; also known as the \u0026ldquo;first agricultural revolution,\u0026rdquo; (Meliala et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), involved the transition from hunting and gathering to settled farming. In the 18th century the \u0026ldquo;second agricultural revolution\u0026rdquo; termed the British agricultural revolution (Simpson \u003cem\u003eet al.\u003c/em\u003e, 2004) initiated and was marked by increased agricultural production due to mechanization and improved land productivity (Liu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The \u0026ldquo;third agricultural revolution,\u0026rdquo; often referred to as the Asian Green Revolution, occurred in the 1960s and brought advancements like hybrid seeds, irrigation systems, pest control, and synthetic fertilizers (Rose \u003cem\u003eet al\u003c/em\u003e., 2018). Most recently, we have entered into the Agriculture 4.0 era (Fuglie et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ara\u0026uacute;jo et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which is known as the \u0026ldquo;fourth agricultural revolution,\u0026rdquo; integrates advanced technologies into farming practices.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSince the year 2002 when introduction of digitization was felt in the agriculture sector the industry has started integrating this new phenomenon. It introduces a new revolution of agriculture commonly referred to as Agriculture 4. 0 (Raj et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Technological advancements are blending the process of agriculture making the sector a very relevant one. Defined broadly, \u0026ldquo;Digital agriculture\u0026rdquo; refers to the expansion of the IoT\u0026rsquo;s applications, big data analysis, cloud computing, AI, and highly developed robotics in the agricultural domain (Bollini et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bolfe et al., 2020; Abbasi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). On the same note, smart farming also seeks to increase productivity and effectiveness by lessening the detrimental repercussions of farming activities on the environment (Duncan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mukherjee et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, with the help of these technologies, Agriculture 4. 0 ensures the rational use of inputs like fuel, fertilizers, seeds, and herbicides (Raj et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nevertheless, the greatest difficulty in the development of digital agriculture is that of connecting all these mentioned technologies that appear across the different branches of sciences (Iglesias et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This makes overcoming the integration issues a key to the advancement of Agriculture 4. 0. The advancement that we discover in the framework of digital agriculture also has drawbacks.\u003c/p\u003e \u003cp\u003eThus, to achieve a sustainable perspective for Agriculture 4. 0, one has to understand the effects of this concept as well as its advantages and disadvantages (Klerkx et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Abbasi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To date, the use of information technologies in agriculture is one of the topical issues of scientific discussions (Muhuri et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; da Silveira et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such technologies have the capability of cantering for environmentally friendly measures in order to enhance food security. Recently, improving the method of the use of limited inputs in agricultural production has become the subject of intense interest among many scholars (Bertoglio et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Mukherjee et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, it is still necessary to note that in this process of shifting to new technologies, human activity is still paramount in making appropriate and vital decisions. Unfortunately, very often one can have an impression that farmers just watch this revolution happening (Bollini et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Industry 4.0 with its technology packages of can make unforeseen social, environmental, and economic impacts on agriculture (Arifin et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, comprehending Agriculture 4.0 involves understanding its core themes, which centre around the integration of these technologies in realm of agriculture (Latino et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although there is no definition of digital agriculture with is universally accepted, research in this area has been growing rapidly (da Silveira et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study aims to explore and elucidate the central themes of Agriculture 4.0 through a thematic approach. Adopting emerging technologies is vital for the advancement of Agriculture 4.0 (Lioutas et al., 2022). However, the specific subjects covered within this field remain somewhat ambiguous. This study seeks to clarify these themes and provide a deeper thoughtful research paradigm in digital agriculture.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBibliometric analysis of Agriculture 4.0 to understand research trends, influential works, and key areas of focus in this emerging field. This analysis aids researchers in identifying pivotal studies, collaborations, and gaps in the literature. The outcome offers a comprehensive overview of current advancements, enabling informed future research directions.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eA thorough analysis of bibliometric data was done to thoroughly explore the domain within the framework of Agriculture 4.0. Groos and Pritchard (1969) coined the term \u0026quot;bibliometric analysis\u0026quot; to describe a set of quantitative methods for tracking and analysing the spread of literature on a particular topic (Roemer, 2015; Zulfah and Astuti, 2023). This quantitative method examines and evaluates bibliographic resources within a specific scientific field, including publications, citations, authors, and institutions (de Sousa, 2021; Miltos et al., 2023). A comprehensive and methodical assessment of various bibliographic components, including publication numbers, authors, journals, keywords, and citations, is part of the methodology. Scientists can uncover significant connections, patterns, and trends in Agriculture 4.0 by investigating these factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevelopment of a Theme and Objective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore commencing bibliometric analysis within the realm of Agriculture 4.0, it\u0026apos;s imperative to delineate clear research inquiries and objectives. Following an exhaustive review of literature, the study recognized these subsequent questions:\u003c/p\u003e\n\u003cp\u003ea. What are the predominant study trajectories within the context of Agriculture 4.0?\u003c/p\u003e\n\u003cp\u003eb. Who are the most productive authors within the agricultural sphere, particularly concerning Agriculture 4.0?\u003c/p\u003e\n\u003cp\u003ec. What are the prevalent keywords utilized by authors in the domain of Agriculture 4.0?\u003c/p\u003e\n\u003cp\u003ed. Which institutes wield significant influence in advancing Agriculture 4.0?\u003c/p\u003e\n\u003cp\u003ee. How does research on Agriculture 4.0 diverge across different regions or nations?\u003c/p\u003e\n\u003cp\u003ef. What are the commonly utilized Agriculture 4.0 technologies, and how have they evolved?\u003c/p\u003e\n\u003cp\u003eg. Which publications receive the most citations in the realm of Agriculture 4.0 how do they shape the trajectory of the field?\u003c/p\u003e\n\u003cp\u003eh. To what extent does Agriculture 4.0 research within the ambit of contributing to specific research categories and long-term objectives?\u003c/p\u003e\n\u003cp\u003eThese research queries are adaptable and subject to modification, analysis, and tailored to suit exclusive study interests and objectives. Subsequently, the next step entailed formulating a specific search strategy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDatabase selection and search queries\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatabase selection and search query processes are crucial in information retrieval systems (Groos and Pritchard, 1969; Tobias et al., 2021). Effective database selection for keyword-based searches across multiple structured data sources involves summarizing keyword relationships in relational databases to choose the most suitable databases for a given query (Bei et al., 2007). The importance of selecting appropriate databases for systematic reviews emphasizes the need for a systematic approach to minimize bias and enhance the validity of search results. Key databases for Agriculture 4.0 research include Scopus, Web of Science, Dimensions, Lens, and Google Scholar. Dimensions database is advantageous as general access as compared to Scopus or Web of Science which incur costs. Thus, open-access data was used for the present study. Dimensions are commonly used for citation analysis, and research objectives are typically used to guide database selection (Aliche et al., 2020). The Dimensions database provides a huge amount of raw data, but the cleaning of data is difficult and time-consuming. We carefully selected search phrases like \u0026quot;Agriculture 4.0,\u0026quot; \u0026quot;Smart Farming,\u0026quot; \u0026quot;Farming 4.0,\u0026quot; and \u0026quot;Digital Agriculture\u0026quot; to ensure papers were relevant to the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSearch and sieve the publications\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe search was then conducted to obtain articles that were further reviewed according to the quality and their relevance (Gusenbauer and Haddaway, 2020; Lefebvre et al., 2019). The subject was basically searched for English articles and only titles and abstracts of the research papers where consulted. No bias was carried out in data downloading, and all the relevant data were downloaded on the same day. 2,389 documents formatted in CSV were found during this search; the ones that were unwanted or contained incomplete articles were optionally eliminated. Another essential process in bibliometric analysis is the data cleansing process through which irrelevant or low-quality data is eliminated to enable high-quality relevant data for response to research questions most of the time (Andersen 2013, Linnenluecke, 2020). Data cleaning is essential in order to get proper and meaningful outcome even if it involves time and efforts (Osborne, 2012; Ganti and Sarma, 2013). Consequently, the available number of papers to perform bibliometric analysis was 1,458 only.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalyze the bibliographic data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This study focused on bibliographic data which involved aspects such as authors, year of publication, titles of the journals and citation indexes (Meho and Yang, 2007). For this purpose, various bibliometric indices, like the number of publications, citation rates, and results from the frequent word analysis were used (Ellegaard and Wallin, 2015; Kalantari et al., 2017). Further, bibliometric analyses such as co-citation, co-authorship, and bibliographic coupling analyses were also employed (Song et al., 2023). For bibliometric analysis R programming as well as the Biblioshiny package (Aria \u0026amp; Cuccurullo, 2017) was used in this study. For the network analysis and visualisation, we employed one of the strongest tools known as VOSviewer which facilitated mapping of the relations between the publications and authors. Thus, with the help of this tool, the identified patterns and trends in the field were big in terms of Agriculture 4.0. Precisely, the analysis helped to recognize the most important authors, articles, and topics in the given citation pool. The approach used in the present research not only pays attention to the role of individual authors and works but also uncovers patterns and processes at the level of the scientific field. Therefore, the present research fosters a significant reference material for scholars and professionals who strive to address the challenges and embrace the opportunities of the dynamically transforming field of Agriculture 4. 0 effectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpret the findings\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter the bibliometric data analysis, we briefly compared and contrasted our findings with the objectives set for the study. For Agriculture 4.0 key themes, important authors, and research collaborations that explain the current state of the field and specify the topics that require further study were identified. The scrutinized activity identified research collaborations and the lack of research on certain topics, which will determine further work on important themes. These findings are most useful to inform this study and determine areas that needed more researches (Mishra et al., 2016; Abdullah et al., 2023). The adoption of this method enhanced the knowledge of Agriculture 4.0 and new avenues of development in the specific field have been created (Pizzi et al., 2020).\u0026nbsp;\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eTrends in Publications\u003c/h2\u003e \u003cp\u003eThis is another way through which we have comprehensively studied Agriculture 4. As for results obtained from analysis of the Dimensions data, there was no research indicated. This is a critical advancement that provides evidence of the ever-evolving interest and expansion of this discipline and the field of study in general; namely the level of publications. We monitored this development over the years with bibliometric analysis, which is a valid method to identify a growth or shrinkage of research topics (Wang et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The examination of the number of publications per annum tells more about how Agriculture 4. 0 research is progressing. This information is quite useful in ascertaining which research area is gaining or losing popularity hence aiding in the direction of resources to the most rewarding and worthy research areas. Writing in 2019, Gautam considers bibliographic analysis as a method for evaluation of the research productivity and for identification of the cross-disciplinary impacts. That is why it is used in Agriculture. It is disturbing that to a large extent 0 research has yielded findings that may be useful for additional inquiries and in establishing blueprints for the deployment of resources. The Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the publication trend over the duration of the research from the year 2015 to the year 2024. Based on the trend established above, annual individual publications are rising gradually, with the highest number recorded in 2023. Such a pattern indicates tremendous improvement in productivity of the area of concern.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCitation analysis was done to evaluate the research's impact, the outcomes are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The graph unequivocally shows that there have been more citations annually, and that this increase has been happening at a faster rate over time. The increasing number of citations that the research has received indicates that the work done in the field has become more and more influential. As a result, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents a more intricate perspective on the research impact. than Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which displays a productivity metric. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the field has produced significant research that has attracted the scholarly community's increasing attention and recognition. Research impact and quality are evaluated, as well as patterns and trends in scientific publications, using bibliometric analysis. The popularity and impact of scientific works are reflected in the number of citations that are received annually. It illustrates how study trends have changed over time. When assessing an academic's or research group's performance, bibliometric analysis often relies on the volume of publications as a primary metric. The quantity of publications is widely used to compare researchers or academic groups and is thought to be a useful indicator of research productivity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this study, we focused on the number of published works across multiple scientific realms. The total amount of publications in each category is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e. According to the data, information and computing sciences gain the maximum number of publications. The other large spheres are agriculture, veterinary sciences, and food science. It's important to recognize that the number of publications does not always correspond to the quality of research or its relevance. Meanwhile, the data that covers other bibliometric measures such as the impact factor of journals, trends of co-authorship, and citation accounts might show specific areas that make substantial progress in their respective spheres. As such, they provide a comprehensive assessment of research productivity and its impact. Therefore, a variety of indicators are necessary for a comprehensive bibliometric analysis. Furthermore, the research topic influences how relevant the quantity of publications is. As a result, while publications are an essential part of bibliometric analysis, adding more indicators results in a more accurate and thorough assessment of research performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMore specifically, further literature searches were performed on articles concerning the SDGs. The aim of the bibliometric analysis was to identify, assess, and review relevant research studies in order to determine achievements in implementing the seventeen Sustainable Development Goals that were endorsed by the UN in 2015. The bibliometric analysis of publications related to the SDGs helps identify the gaps in the research as well as new trends (Raman et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It also presents the mapping and cooperation between the researchers and institutions involved in SDGs (Trane et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The results are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e, which presents the results of the publications classification based on the themes of studies in order to determine the distribution of the research output across the thematic areas of SDGs. This paper assists in identifying the type of region that should be focused on for reaching these goals and gives valuable information regarding the research fields that are engaged in the advancement of the SDGs. The graph that displays the theme distribution of the publications regarding Agriculture 4.0, which is connected to SDG 2: As can be seen from Issues such as Zero Hunger and affordable and Clean Energy, amongst others, stresses how critical publication is in determining funding and policy as well as research initiatives purported to support the accomplishment of the SDGs. By leveraging advanced technologies and innovative practices, Agriculture 4.0 aims to enhance food security, improve agricultural productivity, and ensure sustainable energy use in agricultural processes. To assist policymakers and funders in noticing domains that should be researched in further detail to advance the progress, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e has arranged the publications according to the major SDG categories. Based on this, in the SDG bibliometric analysis, publications are central to the former to generate evidence that can facilitate decisions needed in the attainment of development goals that are sustainable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Productive authors","content":"\u003cp\u003eThe most productive authors in the specified field were identified in accordance with the data obtained from the Dimensions database. The volume of citations received for their work was a measure of its significance. As for productivity, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows ten authors involved in our annotated bibliography ordered by the number of citations, or frequency. The paper presents valuable comprehension of the leading scholars in the given area of study and their significant findings to knowledge escalation (Rita et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The authors are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in the decreasing order of citation index obtained from Google Scholar. This way, citations per publication helped assess an author\u0026rsquo;s genuine impact more effectively. The research calibre of the authors was defined further by the analysis where authors with more than 700 citation had cited their papers more frequently. The number of times a publication has been cited in other scholarly works, rather than just the raw citation totals, is vital as a bibliometric index in comparing the productivity of an author\u0026rsquo;s work and leads to greater precision in measuring the impact. Diagnostic tests; (Bornmann et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Durieux and Gevenois, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Candidates with higher citation per publication scores are likely to have a sizable impact in their discipline, in variance and policy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 authors worldwide in terms of productivity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of documents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. of citations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCitation per publications\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmma Jakku\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e147.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaurens W A Klerkx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e133.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanagiotis G Sarigiannidis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLei Shu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAshok Kumar Das\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJames Alan Turner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmily Duncan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCallum R Eastwood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvan D G Fraser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMichael S Carolan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAuthors\u0026rsquo; production over time\u003c/h2\u003e \u003cp\u003eIt is essential to evaluate an author's productivity and impact in order to determine their relevance in a given field. The top nine authors over the previous 12 years in terms of productivity are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The quantity of papers produced determines productivity, whereas annual citations indicate impact. The colour depth of a circle signifies the annual citation count, while the circle's size corresponds to the number of publications. Klerkx and Lee are the most productive, according to the graph, with Chen contributing consistently from 2012 to 2024. This bibliometric analysis does a good job of illustrating these researchers' productivity and influence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eJournals\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the findings of a citation analysis that was done to determine the impact of publishing sources. The most papers were published in the IOP Conference Series: Earth and Environmental Science, which was followed by Sensors and Computers and Electronics in Agriculture, according to a citation analysis. Agricultural Systems and Sensors was the second most cited topic overall, after Computers and Electronics in Agriculture. Though the impact of the sources varied, Agricultural Systems had the highest citations per paper (CPP), followed by Computers and Electronics in Agriculture and the Journal of Rural Studies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 17 sources around the globe.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDocuments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCitation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCitation per paper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIOP Conference Series Earth and Environmental Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComputers and Electronics in Agriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSustainability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIEEE Access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgronomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternational Journal for Research in Applied Science and Engineering Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApplied Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE3S Web of Conferences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural Systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e141.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJournal of Physics Conference Series\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJournal of Rural Studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultimedia Tools and Applications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcedia Computer Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecision Agriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmart Agricultural Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCo-authorship analysis of authors\u003c/h2\u003e \u003cp\u003eAnother method that is often used to establish how authors collaborate in the research society is co-authorship analysis (Alireza et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This method provides analysis of the structural organisation of the community, in which the authors are located in the node while collaboration is represented at the edge of a network, according to Andrew (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Degree centrality, betweenness centrality, and clustering coefficients are applied to work with these networks (Silvia et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mitrović et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While degree centrality is calculated as the number of direct connections made by an author, and betweenness centrality is used to highlight an author's function as a link between various subgroups, clustering coefficients, on the other hand, are used to identify closely-knit research groups. Thus, this research aims to provide significant knowledge of the social and intellectual structure of the field to determine authoritative writers, established research groups, and connections between them. Furthermore, the analysis will point out the new and developing research topics and directions. Degree centrality deals with self-co-authorship connections, betweenness centrality accounts for interconnection and clustering coefficients represent density and authors\u0026rsquo; clustering. This analysis helps identify key authors, research clusters, and potential research gaps or collaborations. In Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e, a network of co-authors is shown, with each node representing an author, links indicating co-authorship, node size showing publications, and node colour indicating author country. Research groups that are closely related to one another are displayed in the network as node clusters with similar colours. This bibliometric analysis identifies authors with significant network influence, seen in their high degree and betweenness centrality. These authors bridge research groups, fostering collaboration and knowledge exchange, and driving Agriculture 4.0 research synergies. We identified 12 non-interconnected clusters but found three closely related groups. Co-authorship analysis also identifies collaboration types like intra-institutional, inter-institutional, international, and interdisciplinary offering insights into collaboration dynamics and influencing factors. This analysis, performed with VoSviewer, found 462 authors meeting the criteria, 39 related, and 8 clusters showing authors' collaborative intellectual spaces.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCo-authorship analysis of countries\u003c/h2\u003e \u003cp\u003eTo Bornmann and Wohlrabe (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) the comparative analysis of the citation of publications of various national origin is made feasible by normalised citation values that are derived from networks of visualized maps created by VOSviewer. The every figure in these maps illustrates a country and the size of this figure indicates the importance and quality of the country\u0026rsquo;s production in terms of normalised citation value. According to the discussion, research output of a nation studied is scientifically valuable if the node size of the nation is larger. Therefore, it provides a significant measure which can explain how effective various nations are relative to the results of productivity and the impact of the study. We also carry out a country co-authorship analysis based on the same dataset, thus using fundamental criteria to clean up data and establish a solid identification of groups of collaborating countries with one another. To satisfactorily address all the sampled criterion\u0026rsquo;s facet, at least five documents were needed for each country; only 61 out of 100 countries complied with this aspect. The map is visualised in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003e, which displays eight groups of nations that have established strong cooperative ties. Labels and circles denote the various countries on the visualization map, along with circle size corresponding to the number of documents produced by each nation. Greater impact in particular studies is indicated by larger circles and labels. India stands out for its research output and impact, and other nations have clustered around it, demonstrating a strong collaboration network between them. The clustering and interconnectedness of the circles suggest robust international cooperation, with certain countries like India, the United States, and China serving as central hubs around which other nations cluster.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCountry Scientific Production\u003c/h2\u003e \u003cp\u003eThe scientific output level in terms of research publications related to Agriculture 4.0 is indicated by the blue colour on the map of the country's scientific production. This visualization showcases the countries contributing significantly to the advancement of knowledge in this field. It's intriguing to see how nations with robust research output in Agriculture 4.0 engage in collaborative efforts, transcending geographical boundaries for mutual scientific benefit.\u003c/p\u003e \u003cp\u003eFor instance, India, China, the USA, and the United Kingdom have demonstrated notable scientific production in the field of Agriculture 4.0. These countries have engaged in significant research output, contributing substantially to the global body of knowledge. While such scientific production may not directly address the development theme, it can facilitate the exchange of policies and knowledge and stimulate market collaborations. Thus, advocating for international scientific production to bolster innovation and knowledge dissemination is paramount, as it fuels advancements and fosters development within the field.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe nation with the most citations was ascertained by performing a citation analysis on all the countries. The results indicate that the USA has the greatest citations (285), followed by Australia, the UK, Korea, and New Zealand.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCitation Analysis\u003c/h2\u003e \u003cp\u003eThe primary applications of co-citations are the evaluation of scientists, publications, and scientific institutions (Rousseau and Zuccala, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Citation analyses can be used to identify various disciplines and emerging specialities. In addition, they can determine the transdisciplinary or multidisciplinary nature of research programs and initiatives by examining journal relationships (Morillo, 2003). Citations are linked together through co-citations, which are analogous to word co-occurrence metrics (Boyack and Klavans, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUsing citations in a paper as a basis for developing links with other researchers. It is a piece of data that is more than just bibliographic data. A citation signifies the author's choice to highlight the connection between another author's work and their own within their text at a specific point. According to Shaw (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1983\u003c/span\u003e), \"citation develops a relation between authors, which measures the degree to which they interact indirectly\". The visual map of the sources' and organisations' citation analyses is displayed in Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003e and \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e11\u003c/span\u003e, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCitation Analysis of Organisation\u003c/h2\u003e \u003cp\u003eTo better understand how organisations cite one another, a minimum of five papers from each organisation were set. Nine clusters and a total of 75 related organisations were identified based on the criteria. The universities with the greatest impact were Wageningen University and Research, International Hellenic University, University of Guelph, and Anna University in terms of total citations. This suggests that these universities play a significant role in the field, contributing substantially to the body of knowledge and influencing other researchers and institutions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCitation Analysis of Sources\u003c/h2\u003e \u003cp\u003eCiting a document in a reference list, according to Egghe and Rousseau (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), signifies that the author believes there is a connection such as a similarity in topic, problem, or methodology of the referenced with cited materials. Citation analysis was emphasised as the field that investigates these relationships. It is essentially able to identify disciplines and newly emerging specialties, to determine whether research programmes and initiatives are transdisciplinary or multidisciplinary, by utilising journal relationships. Co-citation is similar to word co-occurrence similarity metrics, connects cited documents together (Boyack and Klavans, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Document co-citation networks were developed to demonstrate the variety of documents and communities within the systems thinking literature (Trujillo and Long, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The citation analysis was conducted using a criterion that required each source to have a minimum of five documents. 48 sources met this criterion, yielding 7 clusters. Each bubble's size indicates the influence of a particular source. Figure\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e11\u003c/span\u003e makes clear which journals have received the most citations and publications within these clusters: Sensors, Sustainability, and Agronomy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCo-citation source analysis\u003c/h2\u003e \u003cp\u003eCo-citation has three main uses, according to Rousseau (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) information retrieval and search; modelling historical progress of science and technology; and assessment of scientists, journals, and scientific organisations using both qualitative and quantitative methods. Essentially, citation analysis can identify disciplines and new fields of study, as well as whether research projects and programmes are transdisciplinary or multidisciplinary, by utilising journal relationships. Co-citation, which is comparable to word co-occurrence metrics in similarity analysis, connects cited documents. After the co-citation analysis of publication sources was completed, 298 out of 8149 sources met the threshold. Six clusters in all were identified by the analysis. Based on Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e12\u003c/span\u003e, we can deduce that the journals Computer and Electronics in Agriculture, Agricultural Systems, NJAS Impact in Agricultural and Life Sciences, Remote Sensing, etc. had the greatest quantity of citations and publications among the clusters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eKeyword Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThrough keyword analysis, we can learn which words authors believe are crucial to their papers. Based on the terms selected by the researchers for the manuscript's title and abstract, we performed a keyword analysis for this analysis. The main cause of this is that we are unable to perform keyword analysis with the Dimensions data that we were able to retrieve. Using keyword analysis has a few benefits because it provides insight into popular research topics. The research direction within the study's theme will also be made clear by the analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e13\u003c/span\u003e displays the keyword analysis results. The criteria for this analysis required a minimum of 60 occurrences of a term. Out of 32,174 terms, 113 met this threshold. By calculating the relevance score using the default 60% of key terms, we selected 68 terms. Ultimately, 58 keywords were chosen and grouped into two clusters. The keyword analysis results clearly show that 'Smart Farming' is the most prominent term. Other frequently highlighted terms include 'Internet,' 'IoT,' 'Model,' 'Sensor,' 'Network,' 'Development,' and 'Digital Agriculture,' reflecting the current research trends in Agriculture 4.0 technologies and their applications.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study provides a comprehensive overview of research papers on Agriculture 4.0 using bibliometric analysis. The analysis involved authorship, co-authorship among countries, and citation sources, revealing an increasing trend in publications peaking in 2023, alongside a moderate but growing citation rate indicating rising scholarly influence. Key research subjects include information and computing sciences, agriculture, veterinary, food sciences, and engineering, reflecting a strong focus on digital integration and improved agricultural practices. Additionally, Agriculture 4.0 research aligns with Sustainable Development Goals (SDGs) like SDG 2 (Zero Hunger) and SDG 7 (Affordable and Clean Energy), stressing its relevance in addressing global sustainability challenges. Prominent keywords such as \"Smart Farming,\" \"IoT,\" and \"Digital Agriculture\" illustrate current research trends. Top authors, including Emma Jakku and Laurens W.A. Klerkx, have made significant contributions, as evidenced by high citation counts and impactful publications. Key journals, such as \"Computers and Electronics in Agriculture\" and \"Sensors,\" are pivotal in disseminating influential research. This bibliometric analysis offers valuable insights for guiding future research, resource allocation, and policy-making in Agriculture 4.0. It is crucial to promote these technologies in developing countries to increase agricultural output and improve efficiency. Developments in engineering technology will further support the development of automated and controlled agricultural systems. Although the scope of the search results was limited, this study paves the way for future research, necessitating more large-scale and impactful studies. Policymakers and researchers are encouraged to develop further studies and collaborate with other organizations to help achieve SDG goals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets produced and analyzed in this study are accessible within this manuscript. All unique findings from this study are detailed in the article and supplementary materials. For more information, please reach out to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbasi R, Martinez P, Ahmad R (2022) The digitization of agricultural industry\u0026ndash;a systematic literature review on agriculture 4.0. Smart Agricultural Technol 2:100042\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdullah KH, Roslan MF, Ishak NS, Ilias M, Dani R (2023) Unearthing hidden research opportunities through bibliometric analysis: a review. 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A (2023) Analisis bibliometrik terhadap kemampuan pemahaman konsep matematis. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.31004/jerkin.v2i1.83\u003c/span\u003e\u003cspan address=\"10.31004/jerkin.v2i1.83\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Agriculture 4.0, Farming 4.0, Smart Farming, Digital Agriculture, bibliometrics","lastPublishedDoi":"10.21203/rs.3.rs-4948484/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4948484/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe term \"agriculture 4.0\" refers to integrating artificial intelligence, big data, cloud computing, the Internet of Things and advanced robotics into agriculture. The field of Agriculture 4.0 research has seen a surge in attention as sustainable agriculture has gained more prominence. This study concentrated on conducting a bibliometric analysis of Agriculture 4.0 and its growth. The Dimensions.ai data used in the study was produced using the search terms \u0026ldquo;Agriculture 4.0,\" \"Smart Farming,\" \"Farming 4.0,\" and \"Digital Agriculture.\u0026rdquo; A comprehensive dataset consisting of 1,458 relevant documents has been identified, retrieved, and compiled into a CSV format for further analysis. The retrieved data was visualized and analyzed using suitable software. It was that the information and computing sciences field had the maximum number of publications on Agriculture 4.0 (1,015), followed by Agriculture, veterinary and food science (487). The majority of articles (1,074) addressed Sustainable Development Goal 2, which has hunger as its main focus. Based on co-authorship analysis, India, China, and the USA emerged as the leading nations both in impact and research volume, with other countries clustering around them. The University of Guelph, Wageningen University and Research and Anna University were the three organisations with respectively the most impact in terms of total citations. According to the sources' citation analyses, readers were more influenced by the \"Computers and Electronics in Agriculture\" publication when it came to Agriculture 4.0 research. The Agriculture 4.0 research involves many stakeholders; thus, a broad multidisciplinary approach is necessary. Hence, to solve the issue of Agriculture 4.0, multidisciplinary researchers ought to collaborate rather than act alone.\u003c/p\u003e","manuscriptTitle":"Mapping the Evolution of Agriculture 4.0: A Bibliometric Analysis of Research Trends","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2025-01-03 16:46:31","doi":"10.21203/rs.3.rs-4948484/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2024-08-27 07:19:49","doi":"10.21203/rs.3.rs-4948484/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7ac42748-5b7e-4d83-b8ed-641eb5183e98","owner":[],"postedDate":"January 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40972926,"name":"Agricultural Engineering"},{"id":40972927,"name":"Agroecology"},{"id":40972928,"name":"Environmental Engineering"}],"tags":[],"updatedAt":"2024-08-27T07:19:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-03 16:46:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-4948484","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4948484","identity":"rs-4948484","version":["v2"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}