The foundation for precision agriculture relies on Global Positioning System (GPS), which provides accurate positioning for data collection and implementation. Oksanen and Backman (2013) utilized GPS for tractor navigation systems, reducing labor inputs. Tijmen Bakker et al. (2011) developed a real-time Kinematic Differential Global Positioning System (RTK-DGPS) autonomous navigation system, which showed that autonomous robots in field navigation enhance farming efficiency and increase crop yields. Blok et al. (2019) employed a 2D laser radar scanner for robot navigation between rows. Llorens et al. (2011)used laser radar to scan fruit trees, estimating canopy volume with notable detection and navigation effects in orchards.

Remote sensing is also used to acquire real-time information on agriculture. It involves monitoring farmland with sensors mounted on aircraft and satellites and collecting information about crop growth and soil conditions (Khanal et al., 2017; Mulla, 2013). Compared to traditional monitoring methods, remote sensing technology offers advantages such as low cost, strong timeliness, minimal atmospheric interference, and high resolution, creating new tools for agricultural data collection. Bodrito et al. (2021) showed that satellite remote sensing and drone technology can effectively predict crop yields and implement precise fertilization strategies. Moreover, the application of sensor technologies allows farmers to collect critical data on soil moisture, temperature, and nutrient levels, enhancing the granularity of agricultural management practices (Li et al., 2024; Teixeira et al., 2023). Agricultural remote sensing has expanded from merely monitoring crop growth to include precise fertilization, irrigation, and other areas. According to the International Drone Association, the application of drones in the civil sector is expected to contribute $82.1 billion to the U.S. economy from 2015 to 2025, with over 80% coming directly from agriculture (Zhang H. et al., 2021).

The IoT and Geographic Information Systems (GIS) analyze the data collected from GPS and remote sensing, enabling real-time monitoring of farmland and forming a spatial information database that supports precision management. IoT technology uses sensor networks to continuously collect data on soil moisture, temperature, light intensity, and more, providing a rich data source for farmers to make informed decisions based on environmental conditions (Finger et al., 2019; Tzounis et al., 2017; Asim et al., 2024). Big data analytics enhances this process by analyzing vast amounts of data to identify trends and patterns that can inform agricultural practices (Wolfert et al., 2017).

GIS technology excels in integrating and analyzing spatial data, allowing for detailed visualization and assessment of geographic patterns, relationships, and changes over time. Chelaru et al. (2011) used GIS technology to analyze changes in the agricultural environment, providing data for agrarian production forecasting and early warning. Ding et al. (2010) studied soil organic phosphorus based on GIS, predicting the spatial distribution of organic phosphorus in agricultural soils in southwestern Australia and evaluating phosphorus loss conditions.

Machine learning (ML) and AI technology enhance precision agriculture by applying advanced algorithms to conduct in-depth analyses of agricultural data. Machine Learning utilizes advanced algorithms to analyze vast amounts of agricultural data. ML algorithms can identify patterns and correlations within datasets that may not be immediately apparent, allowing for more accurate predictions regarding crop yields, pest infestations, and disease outbreaks (Zha et al., 2020). By continuously learning from new data, these algorithms adapt to changing conditions on the farm, providing real-time insights that help farmers make informed decisions (Dhillon et al., 2023; Kübert-Flock et al., 2023). For example, random forest regression models have been utilized to predict the yields of crops planted in rows based on multisource satellite data, demonstrating the potential for improved forecasting accuracy (Terra et al., 2021).

Artificial intelligence extends the capabilities of traditional agricultural practices by applying complex models and algorithms to support intelligent decision-making. AI systems are designed to analyze data from various sources, including IoT sensors, satellite imagery, and historical agricultural records (Misra et al., 2022). By synthesizing this information, AI assist farmers in developing scientific production plans tailored to their specific conditions and goals. For example, it can recommend optimal planting times, crop rotations, and pest management strategies based on predictive analytics (Kumari et al., 2023). Additionally, AI technologies provide insights into market trends and consumer preferences, enabling farmers to adjust their production strategies to meet demand (Liu et al., 2023).

Precision agriculture requires precise fertilization and irrigation based on the actual conditions of the farmland (Drusch et al., 2012; Sharma A. et al., 2021). Traditional fertilization and irrigation methods often lead to waste and pollution, while precision fertilization and pesticide application technologies effectively address these issues. It selectively applies pesticides based on target presence and characteristics, reducing pesticide deposition in non-target areas (Bongiovanni and Lowenberg-Deboer, 2004; Robert, 2002). Thus, Precision pesticide application technology helps to achieve better outcomes, lower costs, and less environmental pollution while improving crop disease resistance and product quality (Tian, 2002). Back et al. (2014) designed an image-based application rate measurement system to control granular fertilizer application rates. Reyes et al. (2015) developed a variable rate fertilizer automatic control system to enhance fertilization precision. Yu (2019) studied an online testing system for solid fertilizer application rates in a seed fertilizer drill machine, achieving precise measurement of application rates.

Regenerative agriculture is a holistic approach aimed at protecting and restoring food and agricultural systems by promoting soil regeneration, increasing biodiversity, improving water cycles, and enhancing overall ecosystem services. This method addresses degradation caused by industrial and conventional agricultural practices through sustainable farming and grazing techniques that rebuild soil organic matter and foster healthy ecosystems (Muhie, 2022). The benefits extend beyond soil health; they also lead to improved water infiltration and increased microbial abundance, which are essential for effective nutrient and moisture management (Basche et al., 2016). Moreover, regenerative practices reduce production input costs while effectively increasing crop yields, achieving enhanced efficiency and cost-effectiveness (Kaye and Quemada, 2017). Additionally, this approach considers animal welfare, contributing to improved livestock yield and quality (Palm et al., 2014).

In 2009, the Food and Agriculture Organization (FAO) proposed the concept of “climate-smart agriculture” (CSA), exploring a strategy that ensures the sustainability and stability of global agricultural productivity while actively promoting carbon fixation and emission reduction to effectively mitigate the adverse impacts of climate change (Food and Agriculture Organization of the United Nations, 2013). CSA enhances food safety through a multifaceted approach that improves resilience, productivity, and sustainability in food systems, such as knowledge-smart practices, water-smart techniques, soil-smart strategies, and livestock management. Knowledge-smart practices equip farmers with essential tools and information, such as crop insurance and weather-based advisory systems, enabling them to make informed decisions that safeguard their yields (Qureshi et al., 2022). Multispectral and hyperspectral imaging support climate-smart agriculture by helping to identify plant stress and nutrient deficiencies early in the growing season (Lipper et al., 2014). Water-smart techniques, including drip irrigation and rainwater harvesting, ensure efficient water use, minimizing the likelihood of waterborne diseases affecting crops and enhancing overall food quality (Qureshi et al., 2022). Bai et al. (2016) found through APSIM simulations that optimizing planting density, sowing dates, water and fertilizer management, and plant protection measures can significantly improve crop yields and stability.

By implementing soil-smart practices, such as crop rotation and bio-fertilizers, CSA fosters healthier soils that lead to robust crops, thereby reducing the risk of disease and pest infestations. In terms of livestock management, livestock-smart practices focus on improving animal health through better feed and disease management, ensuring that meat and dairy products are safe for consumption (Qureshi et al., 2022). Together, these CSA practices bolster food production and create a safer and more reliable food supply, essential for addressing the challenges posed by climate change and urbanization.

Precision agriculture promotes food safety by enhancing crop yields and improving resource efficiency. By advanced technologies such as satellite imagery, GPS, and IoT sensors, farmers can optimize the application of inputs like water, fertilizers, and pesticides (Drusch et al., 2012; Pandey and Pandey, 2023; Sharma A. et al., 2021). Research shows that these technologies result in higher crop productivity while reducing resource waste (Pesonen et al., 2014). Enhanced soil organic matter and microbial activity, achieved through precision agriculture practices, lead to healthier soils that support more robust crops.

In addition, precision agriculture stabilizes food supply within urban agriculture, thereby improving food safety. Urban agriculture reduces the distance food travels from farm to table, minimizing the risks associated with contamination during transportation. By producing food closer to consumers, urban farms can maintain freshness and quality, ensuring that safety standards are upheld (Rodríguez et al., 2022). Urban farmers can monitor and manage crop growth more effectively with precision agriculture, such as climate-smart approaches and digital decision-support systems, ensuring optimal conditions for production (Ebenso et al., 2022). This localized approach allows for quicker responses to environmental change, directly contributing to more consistent yields.

Moreover, the increased productivity provided by precision agriculture is essential for addressing the food demands of a rapidly growing global population (Tilman et al., 2011). As climate change and urbanization threaten traditional farming methods, precision agriculture offers innovative solutions to increase resilience. By adapting to local conditions and understanding the specific needs of crops, farmers can ensure a stable food supply even in unpredictable environments (Cao et al., 2017; Lin et al., 2023). This stability is critical not only for food security but also for the overall economy, as agricultural productivity directly impacts income and employment in rural areas.

Another significant impact of precision agriculture is its ability to enhance the monitoring of food quality throughout the supply chain. Technologies such as blockchain and big data analytics facilitate improved traceability of food products from farm to table (Kumari et al., 2023). This traceability ensures that safety standards are met at every stage of production, processing, and distribution (Davies and Garrett, 2018; Kimhi, 2024; Misra et al., 2022). For instance, blockchain technology allows for the secure recording of every transaction and movement of food items, creating an immutable record that can be accessed by all stakeholders (Peng et al., 2022).

In the event of a food safety incident, this level of traceability enables rapid identification of affected batches, making it easier to recall contaminated products and prevent them from reaching consumers (Shafi et al., 2022). Enhanced monitoring mechanisms also allow for real-time assessment of food quality, ensuring compliance with safety regulations and standards (Pesonen et al., 2014). This proactive approach both protects consumers and fosters greater transparency in the food system, thereby enhancing consumer confidence in the safety and quality of their food (Pontikakos et al., 2010).

Precision agriculture contributes to food safety by minimizing reliance on chemical fertilizers and pesticides, which have been linked to various health and environmental concerns. Pande and Arora (2019) have shown that excessive chemical use not only poses risks to human health but also adversely affects soil health, sustainability of productivity, and environmental stability. By employing targeted application methods based on real-time data, farmers can significantly lower the quantities of chemicals used, thereby reducing the risk of harmful residues in food products (Bongiovanni and Lowenberg-Deboer, 2004; Robert, 2002).

Through the implementation of integrated pest management and crop rotation strategies enabled by precision agriculture, farmers can adopt more sustainable practices that prioritize natural pest control methods and reduce the need for chemical interventions (Zimmermann et al., 2021). This shift not only enhances the safety of food products but also contributes to environmental sustainability (Miao et al., 2010). Consumers are increasingly seeking products that are free from harmful chemicals, and precision agriculture helps meet this demand by producing healthier food options (Jeong and Bhattarai, 2018).

Precision agriculture enhances food safety by enabling the early detection and management of plant diseases and pests (Jones and Naidu, 2019). Utilizing advanced technologies such as machine learning algorithms, drones, and imaging technologies, farmers can continuously monitor crop health in real time. These systems can detect subtle changes in crop conditions that may indicate the onset of disease or pest infestations before they escalate into significant problems (Grünig et al., 2021; Khanal et al., 2017; Mesías-Ruiz et al., 2023; Mulla, 2013).

This proactive approach allows for timely interventions, which are crucial in protecting crop yields and maintaining food safety (Mohanty et al., 2016). For instance, drones equipped with multispectral cameras can assess plant health over large areas, identifying stress factors that may not be visible to the naked eye (Shahrooz et al., 2020). Early detection systems not only reduce the economic losses associated with crop failures but also mitigate the risk of foodborne illnesses linked to contaminated products (He et al., 2023). By addressing these issues swiftly, farmers can ensure the delivery of safe, high-quality food to consumers, thereby reinforcing the overall integrity of the food supply chain (Kumari et al., 2023).

In developing countries, the adoption of precision agriculture technologies is often constrained by limited resources, infrastructure, and skilled labor. These nations typically operate small-scale and fragmented agricultural systems, which challenge the realization of economies of scale. Consequently, farmers tend to adopt low-cost, accessible technologies that enhance food safety without imposing substantial financial burdens.

Countries like India, which have relatively small and dispersed arable land, have developed a precision agriculture model that focuses on high-value crops combined with selected precision agriculture technologies (Alagh, 2011). For example, Indian tea farmers employ quick soil mapping and targeted labor allocation. The structured nature of tea planting allows for quick soil mapping once the conditions of each plot are understood. Labor can be organized based on the specific characteristics of each field, allowing for tailored interventions that compensate for technological limitations (Pathak et al., 2022). This approach not only improves productivity without requiring substantial financial investment but also ensures that the crops are cultivated under optimal conditions, thereby enhancing food safety through better quality control.

Additionally, low-cost diagnostic tools, such as chlorophyll meters and leaf color charts, have become vital for smallholder farmers in countries like the Philippines and Indonesia. These tools help farmers monitor nitrogen levels in crops, which is crucial for producing safe and nutritious food (Mazloumzadeh et al., 2010). By enabling farmers to make informed decisions about fertilization, these technologies directly contribute to food safety.

In South Africa, small-scale farmers utilize mobile phone data to monitor soil moisture conditions, allowing them to make timely agricultural decisions that optimize crop health (Dlamini et al., 2023). By enabling farmers to make informed decisions about fertilization, these technologies directly contribute to food safety (Mondal and Basu, 2009).

In contrast, developed countries like the USA leverage advanced precision agriculture technologies to enhance food safety through large-scale and intensive farming practices. The prevalence of larger farms facilitates the implementation of sophisticated precision agriculture techniques, leading to improved crop yields and safer food products. Governments in these countries prioritize the development of precision agriculture through policy support, funding, and infrastructure investments (Daberkow and McBride, 2003). The focus on data-driven decision-making allows farmers to optimize inputs and maximize outputs effectively, which underpin food safety initiatives (Basso et al., 2001).

In the USA, pilot demonstrations of precision agriculture technologies are conducted to assess their benefits, particularly concerning food safety. Researchers analyzed data from comparative experiments to evaluate the economic and safety impacts of new technologies. Universities play a crucial role in showcasing the advantages of precision agriculture, helping farmers understand how these methods can reduce the use of harmful chemicals while maintaining high food safety standards (Yost et al., 2017). This direct approach to showcasing the benefits of precision agriculture has proven to be an effective method for technology dissemination. For instance, soybean farmers in the USA utilize remote sensing technologies such as satellite imagery and drones to monitor crop health. This enables them to significantly reduce chemical usage while enhancing food safety and yield quality (Arjoune et al., 2022).

In Denmark, experts from the National Agricultural Advisory Center provide guidance on precision agriculture technologies, ensuring that local agricultural advisory agencies receive the latest knowledge. This close connection between specialists and farmers fosters the adoption of best practices, thereby improving food safety standards in the region (Pedersen et al., 2004).

Japan’s focus on developing portable precision agriculture devices reflects the need for accessible solutions in smaller farmland areas. Innovations like sensitive soil moisture sensors and precise fertilization machines are tailored for small-scale farms, promoting food safety by enhancing management practices and reducing the risk of over-fertilization (Shibusawa, 2001).

To minimize the risk of missed or misdirected searches, the study implemented the search strategy in Table 1. This process started with a selection of relevant literature, utilizing the Web of Science (WoS) database, which is recognized for its quality and extensive coverage of scholarly content (Yadav and Banerji, 2023). This database was widely recognized as the most esteemed and extensively used data sources on publications and citations, providing comprehensive access to worldwide prestigious research (Birkle et al., 2020; Martín-Martín et al., 2021). The choice of a single database such as WoS reduces the complexity and potential human error associated with consolidating data from multiple sources (Donthu et al., 2021).

The “Search Keywords” column employed combinations including (“precision agricultur*” or “precision farming*” AND “food safety*” or “food security*” or “food hygiene*” or “food quality assurance*”), ensuring a wide range of relevant literature was captured. This search string consists of two sets of representative terms related to the thematic field of investigation.

To ensure the quality of the selected literature, this study established specific inclusion criteria. The time frame included works published until March 24, 2024, and the search was confined to the “TOPIC” retrieval area, encompassing titles, abstracts, and keywords (Latino and Menegoli, 2022). The study adopted a broad scope, encompassing all meso-level topics linked to the keywords identified in the “Citation Topics Meso” section, aiming to capture interdisciplinary research that intersects with precision agriculture and food safety (Wider et al., 2023). As for “Document Type,” all types (e.g., articles, reviews, conference proceedings) were included to ensure a broad collection of relevant research outputs. The selection process was restricted to articles published in English to enhance accessibility and comprehension among the academic community, given the predominance of English in scholarly communication (Kotobiodjo et al., 2024; Lukambagire, 2024).

The study employed the PRISMA (2020) guidelines to analyze and exclude articles from the dataset (Kotobiodjo et al., 2024; Kraus et al., 2020; Latino and Menegoli, 2022), as shown in Figure 1. Duplicate records were merged to ensure that each article was unique in the sample (Kotobiodjo et al., 2024). Then, articles were screened based on their titles and abstracts to eliminate those that were not relevant to the topic (Latino and Menegoli, 2022; Suchek et al., 2021). This procedure finally resulted in the identification of 416 articles.

Bibliometric analysis can be conducted using a variety of tools, such as CiteSpace and VOSviewer, which offer user-friendly interfaces; the Bibliometrix package in R, which relies on code commands; and Pajek and Gephi, which focus on constructing complex network analyses. Among them, VOSviewer has become an increasingly popular software. It highlights essential similarities and connections across various research domains (Yao et al., 2022), including technology, geography, agriculture, economy, and education. By clustering dispersed knowledge, the software reveals similarities and relevancies across diverse themes, allowing scholars to analyze the relationships between different areas of research (Eck and Waltman, 2010).

Due to its outstanding visualization capabilities and user-friendly interface (Wang and Frei, 2011), VOSviewer 1.6.18 software was chosen as the tool in the current study. This research identified significant publications, research collaborations, hotspots, and academic trends through density, overlay, and network analysis. Density analysis created maps for both publication citation analysis and sources citation analysis, visually representing the concentration of research. Overlay analysis was used to identify authorship patterns. It visualized the temporal dynamics of collaboration among authors, highlighting shifts in research partnerships over time. Network analysis generated maps for multiple dimensions, including organizations co-authorship analysis, countries co-authorship analysis, co-citation analysis, and co-word analysis. Network diagrams illustrated the relationships between authors, institutions, and countries, and the connections between keywords and cited works. These graphs provided a detailed overview of the research landscape, allowing researchers to gain a deeper understanding of precision agriculture and food safety.

Additionally, this study employed a qualitative thematic approach to extract critical insights from the reviewed publications. This process began by grouping the publications into categories based on shared themes and ideas. By doing so, researchers were able to identify common patterns and trends across the different studies. Next, it organized the information from each category in tables and narrative summaries. This allowed for a clear presentation of the feature of each theme. Finally, descriptive labels were assigned to each category, providing a concise way to refer to the different groups of publications. These labels helped to clarify the distinctions between the categories and facilitated further discussion and analysis of the findings (Mashari et al., 2023).

Utilizing a citation threshold of 15 or more, a refined subset of 137 documents was extracted from the original pool of 418 studies for further analysis. Among these, 81 were published in 2020 or later. The top three cited publications were Gebbers and Adamchuk (2010) (658 citations), Atzberger (2013) (597 citations), and Maimaitijiang et al. (2020b) (385 citations). Table 2 showcases the top 10 papers ranked by citation count. These highly cited publications underscore their significant influence on precision agriculture and food safety. Future research should build upon these influential works to advance knowledge and practice in precision agriculture and food safety.

Figure 3 presents a density analysis visualization that maps out the web of document links. This figure intuitively reveals the links and influence of documents through the size and color of the nodes. Notably, “Gebbers and Adamchuk (2010)” was in the red zone, indicating a high link, suggesting that it played a central role in the field. Gebbers and Adamchuk (2010) claimed the top position with 15 links when the studies were arranged based on their connectivity. Following closely were Robert (2002), Cao et al. (2017), and Kendall et al. (2022), each with five links, indicating their significant contributions as well. Furthermore, an analysis of publication years shows that earlier research continued to be highly linked, while more recent publications were gradually gaining attention. This trend suggests that while established works remain influential, newer research is beginning to carve out its place in the academic discourse, potentially driving future innovations and developments in precision agriculture and food safety.

By applying a rigorous threshold criterion, which necessitates each source to have at least one contributing document and a minimum of 10 citations, 125 journals out of the 261 sources were identified. Table 3 underscores the top 10 sources garnered distinction through their towering averages of citation counts. Science from AAAS led the list, boasting an average of 658 citations. Agronomy for Sustainable Development from INRA and Computers & Operations Research from Elsevier followed closely, with 358 and 254 average citations, respectively. These high citation averages indicate that these sources are key platforms for advancing knowledge and fostering collaboration in the field of precision agriculture and food safety.

The graphical presentation in Figure 4 offers a visual breakdown of source density analysis, arranged based on their cumulative link strength. Prominent sources such as “science,” “remote sensing,” and “agronomy-basel” were located in the red zones, underscoring their important impact in the field of precision agriculture and food safety. At the forefront stands Science, published by AAAS, which leads the pack with a formidable total link strength of 24. Following closely in second and third positions are Remote Sensing and Agronomy-Basel, both from MDPI, showcasing total link strengths of 22 and 19, respectively. The analysis also reveals a diverse array of topics covered by these sources, including precision agriculture, food science, and environmental sustainability. The clustering of journals reflects the multidisciplinary character of current research trends, emphasizing the importance of cross-disciplinary collaboration. The role of these influential journals in disseminating diverse research perspectives is critical for advancing knowledge and fostering innovation in precision agriculture and food safety.

Among the pool of 2,101 authors under consideration, a selective cohort of 86 authors was identified based on having contributed at least two documents and garnered at least one citation. Table 4 highlights the foremost 10 authors who have excelled based on their exceptional average number of citations. Fritschi, Maimaitijiang, Sagan, and Sidike occupy the top spots, each demonstrating a remarkable average of 252 citations per author, underscoring their significant contributions and influence within precision agriculture and food security. These authors play pivotal roles in advancing research and innovation in this area. Their work not only contributes to the academy but also has practical implications for improving agricultural practices and food security.

Figure 5 presents a visual representation of the web of co-authorship, constructed based on the authors’ names. Each node represents an author, and the connections between them indicate co-authorship on shared publications. The color gradient—from blue to yellow—represents the publication timeline, with blue indicating earlier years (2018 or before) and yellow signifying more recent years (2024). The visualization reveals distinct clusters of authors who collaborated, indicating a strong network of co-authorship. A foremost example of clusters in the map encompasses seven authors who were particularly prevalent during the year 2023. In another cluster of seven authors, the average year of publication for these seven authors was 2010.33, 2019.5, 2020.5, 2017, 2013.67, 2018, and 2023, respectively. The second-largest cluster, comprising six authors, prominently stood out in 2021. Concurrently, the third-largest cluster, boasting five authors, appeared notably in 2023.5, further underlining the dynamic nature of co-authorship networks and their evolution over time. These patterns underscore the dynamic nature of co-authorship networks and their evolution, suggesting that collaborative research is increasingly important in addressing complex issues in precision agriculture and food security. Additionally, it highlights emerging connections among authors like “ismaili, maryem” and “hittiou, abdelaziz,” suggesting new collaborative efforts in recent years. This trend emphasizes the importance of fostering collaboration as a means to enhance knowledge sharing and advance the field.

The figure illustrates how collaboration can lead to the formation of research teams that focus on certain topics. For example, the collaboration in one of the largest clusters involving Yuxin Miao focused on optimizing fertilizer use in precision farming (Cao et al., 2017; Miao et al., 2010; Zhao et al., 2013). Their research addressed the critical need for sustainable practices that enhance crop yields while minimizing environmental impact, reflecting a major trend in agricultural research. This consistency with previous study of Tian (2002) suggests a growing recognition of the need for environmentally friendly agricultural methods.

The other seven-author cluster containing Maninder Singh Dhillon facilitated the topic on precision agriculture and agricultural decision-making (Dhillon et al., 2023; Kübert-Flock et al., 2023). They focused on remote vegetation monitoring techniques and machine learning in agricultural decision-making, which helps in enhancing crop yield predictions and promoting sustainable agricultural practices. Their work exemplifies the integration of technology in agriculture, highlighting the importance of data-driven approaches to improve outcomes.

Moreover, the joint research conducted by the four most cited authors—Maitiniyazi Maimaitijiang, Vasit Sagan, Paheding Sidike, and Felix B. Fritschi—on using Unmanned Aerial Vehicles (UAVs) and data fusion technologies for crop monitoring had also significantly advanced the understanding of remote sensing methodologies in agriculture (Maimaitijiang et al., 2020a; Maimaitijiang et al., 2020b). Their work also emphasized that technological innovations facilitate improved decision-making processes and enhance operational efficiency in farming. The emergence of these topics highlights a significant trend toward integrating advanced technologies into agricultural practices, which is corroborated by findings from Pierce and Nowak (1999) and Chelaru et al. (2011).

By implementing a criterion requiring a minimum of three publications and five citations per organization, 56 institutions were identified out of the 834 organizations. Table 5 presents an overview of the leading 10 institutions, ranked according to their impressive average citation count. All the first three institutions were originated from the USA. The University of Missouri was ranked top with an amazing average of 168 citations per publication. The University of Minnesota and the Ohio State University followed, boasting 96.67 and 81.25 average citations, respectively. The results suggest that these institutions are prolific in their research output and produce work that is highly regarded and frequently cited by peers, indicating their influence in shaping the research area.

Out of the 56 institutions selected, some were not interconnected, leaving 38 groups clustered into six clusters within the organizational network (Figure 6). Each node represents an organization in the network analysis of these organizations, and the connections between them indicate shared authorship on publications. The color-coded nodes reflect different clusters of collaboration, highlighting interaction among institutions. Chinese institutions stand out as being particularly active in their collaborative endeavors, showcasing a commitment to advancing research in precision agriculture and food safety. Prominent nodes such as “China Agricultural University” and “Chinese Academy of Sciences” were located in central clusters. China Agricultural University and the Chinese Academy of Agricultural Sciences were at the forefront of collaboration, tied for the top spot with each having collaborated extensively, co-authoring projects alongside 11 distinct institutions. Ranked further down the list, the Chinese Academy of Sciences engaged in collaborative efforts with seven diverse organizations. The strong connections among these institutions suggest a robust network of partnerships, which is essential for advancing research initiatives. As the agricultural landscape continues to evolve, such collaborative networks will be crucial for addressing emerging issues related to food security.

Collaborative efforts among various institutions were advancing agricultural practices and forming various research areas. For instance, the partnership between China Agricultural University and University of Minnesota focused on improving nitrogen management in rice cultivation by utilizing UAV remote sensing and machine learning, thereby enhancing precision cultivation and yield predictions (Zha et al., 2020). This aligned with another collaboration between the same institutions, which seeks to optimize nitrogen use in wheat-maize cropping systems while minimizing environmental risks, thereby promoting sustainable agricultural practices that protect natural resources (Cao et al., 2017). Furthermore, the joint research of China Agricultural University and Norwegian Institute of Bioeconomy Research leveraged UAV technology to monitor nitrogen status in winter wheat fields, enhancing remote vegetation monitoring techniques and facilitating effective decision-making in smallholder farming (Chen et al., 2019). This trend toward integrating technology in agriculture reflects a broader movement within the field, as highlighted by Qureshi et al. (2022) and Bai et al. (2016), who noted the increasing reliance on precision agriculture technologies to improve efficiency and sustainability.

Real-time monitoring not only helped in managing inputs more effectively but also equipped farmers with the information needed to adapt to changing environmental conditions. Additionally, a partnership between University of Putra Malaysia and University of Adelaide reviewed the factors influencing the adoption of precision agricultural technologies, providing valuable insights for policymakers to increase technology uptake in agriculture (Tey and Brindal, 2012).

Another significant collaboration between China Agricultural University and Chinese Academy of Agricultural Sciences investigated the impacts of fertilizer intensification in China’s HHH Plains, promoting resource-efficient practices while ensuring sustainable development (Kong et al., 2014). These collaborations not only fostered the development of innovative agricultural solutions but also tackled pressing issues related to food security and environmental stewardship. The increasing number of partnerships signifies a trend toward more holistic approaches to agricultural research, which underscore the importance of collaboration in tackling complex challenges in agriculture.

All 86 countries published on precision agriculture and food safety were included in the analysis. They were ranked by average citations, shown in Table 6. Peru and Austria ranked top two (184 and 166.4 average citations), while New Zealand ranked third (109 average citations). The high citation averages suggest that these nations are leaders in addressing challenges related to agriculture and food security. As the field continues to evolve, the contributions from these nations will be essential for advancing knowledge and practices that promote precision agriculture on a global scale.

Given the disconnected nature of certain countries in precision agriculture and food safety, the resulting national co-authorship network comprises 74 nations, organized into six clusters. Each node represents a country, while the connections between them indicate co-authored papers. The color coding helps identify clusters of collaboration, with different colors representing distinct groups of countries. This arrangement reflects the varying levels of collaboration and interconnectivity among these countries within these specialized fields. Figure 7 presents the network analysis visualization of countries. The USA emerged as a clear leader by a wide margin, co-authoring with 43 countries. India and Germany ranked second with 28 collaborations. Then, England followed with 27 links. This visualization underscores these countries’ essential contributions and influence in the precision agriculture and food safety domain. The connections among these countries suggest robust research networks that facilitate knowledge exchange and joint initiatives, which are critical for tackling food security issues by precision agriculture practices.

The visualization reveals a diverse array of countries involved in research, including both developed and developing nations. For instance, China was prominently highlighted in red. Its connections with various countries, especially the USA, India, and several European nations, underscored China’s role as a central hub for research partnerships. This suggests that China was not only advancing scientific research domestically but was also actively participating in international collaborations. Another large agricultural country, Brazil, represented in green, signified its growing involvement in international research cooperation. The connections with countries like India and Australia indicated that while Brazil’s research influence might be smaller relative to larger nations, its trend of engaging in international collaboration was on the rise.

International collaborations among countries were important in advancing innovative topics and addressing agricultural challenges. For instance, a partnership between Germany emphasized how integrating advanced technologies can optimize resource use and enhance food security (Gebbers and Adamchuk, 2010). Similarly, a collaboration between China and England highlighted the significant role of AI in improving food safety and quality while reducing waste, showcasing the benefits of technological innovations across borders (Liu et al., 2023). Additionally, joint efforts among the USA, Germany, and China showcased the global effort to develop innovative tools for real-time plant health monitoring, contributing to precision agriculture (Wu et al., 2020). Furthermore, a work by researchers from the USA and Australia illustrated how big data can enhance food safety management across international food supply chains (Donaghy et al., 2021). In a review led by India and England (Sharma et al., 2020) explored how machine learning improve agricultural sustainability on a global scale. Meanwhile, a paper co-authored by researchers from Australia and the USA addressed the challenges posed by viral diseases in agriculture and emphasized the need for integrated strategies, illustrating the collaborative effort to enhance global food security (Jones and Naidu, 2019). The economic impacts of UAV technology in agriculture, studied by Germany and China, revealed how these innovations improve efficiency and profitability for farmers (Quan et al., 2023).

These international collaborations illustrate how cross-border partnerships are essential in developing innovative solutions to enhance agricultural productivity and improve food security in an increasingly interconnected world. The rising trend of such collaborations reflects the importance of shared knowledge and resources in addressing complex agricultural challenges, supporting findings from (Lubag et al., 2023), which underscore the necessity of global cooperation in research.