Smart Agriculture and Technological Innovation: A Bibliometric Perspective on Digital Transformation and Sustainability

Abstract

Technological progress in agriculture plays an essential role in enhancing productivity, sustainability, and resilience. This study conducts a bibliometric analysis of the concept of technological progress in agriculture using data extracted from the Web of Science database for the period 1979–2025. The main aim is to identify emerging trends, the structure of collaborative networks, and the influence of research on the development of smart agriculture. The methodology is based on a co-occurrence analysis of keywords, co-author networks, and institutional and international collaborations, providing a detailed insight into the dynamics of research in this field. The results show an accelerated growth of studies on the digitization of agriculture, with a particular focus on technologies such as artificial intelligence, precision agriculture, the Internet of Things, and agricultural process automation. According to the bibliometric analysis, China accounts for the largest share of publications in this field, followed by the United States and Australia. These countries also exhibit high levels of centrality in international collaboration networks, indicating their pivotal role in knowledge production and dissemination. Europe shows a fragmented but active collaborative network, while emerging countries are starting to strengthen their position through strategic partnerships. The findings suggest the need for transdisciplinary collaborations in order to mainstream technological progress in agriculture, emphasizing the importance of policies to support technology transfer and sustainable innovation.

1. Introduction

Agriculture is a fundamental pillar of the global economy, playing a key role in ensuring food security and the sustainability of agricultural ecosystems [1,2]. In the context of climate change, the degradation of natural resources, and increasing demands for production efficiency, the integration of emerging technologies is becoming imperative to optimizing agricultural processes [3,4]. Innovations in digital agriculture, biotechnology, and automation offer viable solutions for increasing productivity and reducing environmental impacts [5,6]. Adapting to new technological paradigms requires not only the modernization of agricultural infrastructure but also the development of integrated strategies to facilitate the transition towards sustainable and smart agriculture [7].

Over the decades, technology in agriculture has evolved significantly, passing through different stages of innovation that have completely transformed agricultural practices [8]. In the past, agriculture was based on traditional methods, where farmers used manual equipment and had limited control over environmental conditions [9,10,11]. Technological progress began with the mechanization of agriculture in the 19th century, when tractors and farm machinery were introduced, revolutionizing production by increasing efficiency and reducing reliance on manual labor [12,13]. This period of mechanization was followed by another significant revolution in the 20th century, known as the “Green Revolution”, which introduced chemical fertilizers, pesticides, and more-productive crop varieties, helping to increase yields and meet the food requirements of an expanding population [14,15,16].

However, in recent decades, the focus has shifted to the development and implementation of emerging technologies such as precision agriculture, biotechnology, and digitization. These technological innovations have not only improved production efficiency but have also opened up new perspectives in terms of agricultural sustainability and climate change adaptability [17,18]. Today, technology is no longer just limited to optimizing production processes but presents one of the major solutions for protecting the environment, reducing resource losses, and improving transparency in the global food chain [19,20]. Smart agriculture is being used to increase agricultural productivity yields, address agriculture-based problems such as food demand, and make farms more connected and smarter [11]. One study [21] estimates that the global smart agriculture market will grow from around USD 15bn in 2022 to USD 33bn by 2027.

Thus, the shift from traditional to modern methods represents one of the most profound transformations in agriculture, with technological innovations becoming the pillars of a more efficient, sustainable, and resilient sector [22,23,24]. This evolution has been necessary to ensure the existence of an agricultural system able to face future challenges, such as climate change, increasing demand for food, and the protection of natural resources [25,26,27].

A notable example of technological progress in agriculture is the development of precision agriculture, which uses sensors, remote sensing, and big data analysis to increase resource use efficiency. Studies show that adopting precision technologies can reduce water consumption by up to 30% and fertilizer use by 20% while increasing agricultural yields by 10–15% [28,29,30]. These innovations make it possible to maintain food security and long-term agricultural sustainability [31].

On the other hand, other tools, such as the integration of artificial intelligence in agricultural drone technology, enable optimized crop monitoring, precise land mapping, the efficient application of treatments, and the automation of flight paths, thus contributing to the development of precision agriculture and increasing sustainability in the agricultural sector [32]. In 2022, the agricultural sector accounted for 11% of the global drone market, with Europe and North America remaining the main sales regions, with market shares of 36.1% (USD 1.3bn) and 32.1% (USD 1.2bn) of total sales [33]. Another study [34] estimates that the value of artificial intelligence in the global agriculture market, valued at around USD 1.7bn in 2023, will grow to around USD 4.7bn by 2028.

Biotechnology likewise offers promising solutions for improving crop resilience to extreme climatic conditions [35]. CRISPR-Cas9 technologies are enabling the development of plant varieties capable of withstanding high temperatures and drought [36,37,38,39]. The CRISPR-Cas9 market [40] was valued at USD 2.94 billion in 2022 and is estimated to reach USD 12.22 billion by 2030, with a compound annual growth rate (CAGR) of 19.52% over 2023–2030.

At the same time, the digitization of agriculture is becoming one of the necessary factors for efficient production management. According to a European Commission report [41], the integration of Internet of Things (IoT) and artificial intelligence (AI)-based technologies in agriculture can reduce production losses by up to 20% and improve crop prediction by 90% [42]. These advances enable better supply chain management and reduce risks associated with climate variability.

In the context of the rapid transformation of agriculture through digitalization, automation, and biotechnology, a bibliometric analysis of the concept of technological progress in agriculture is essential in order to identify research trends, collaboration networks, and the impact of studies in the field [43]. Through this approach, the evolution of research on technological progress in agriculture can be highlighted, including emerging subfields such as precision agriculture, artificial intelligence, farm automation, and agricultural biotechnology. At the same time, mapping academic networks allows the analysis of international and institutional collaborations, providing a clear picture of centers of excellence and the flow of ideas at a global level. Assessing the impact of research, through the analysis of citations and the influence of fundamental works, contributes to the identification of dominant theories and studies with practical applicability.

The need for this study stems from the exponential growth of scientific interest in the digitization of agriculture and the integration of technological progress in this sector, essential for food security and sustainable development. Despite the growing number of individual research studies, comprehensive bibliometric syntheses that provide a clear overview of trends, research directions, key actors, and international collaborative networks are lacking in the literature. The existing studies are fragmented, focused on specific technologies or regions without correlating global developments in the field. This bibliometric analysis therefore fills an important gap in the literature, providing a detailed knowledge map and informing future evidence-based agricultural research and policy decisions. At the same time, our study helps to identify scientific gaps and potential emerging directions, strengthening the theoretical and practical basis for digital transformation in agriculture.

The main research questions are as follows:

RQ1. What is the publication trend of scientific papers on technological progress in agriculture according to the volume of articles identified in the Web of Science database?

RQ2. Which concepts and research topics are frequently associated with technological progress in agriculture according to a keyword co-occurrence analysis?

RQ3. Who are the most active and influential authors in the field of technological progress in agriculture based on their number of publications, citations, and positions in collaborative networks?

RQ4. Which are the academic institutions with most scientific contributions in this field and what impact do they have according to bibliometric metadata?

RQ5. What are the main countries contributing to the scientific literature on technological progress in agriculture and how do they position themselves in global collaborative networks?

The study is structured as follows: Section 1 presents the introduction to technological progress in agriculture, providing an overview of the evolution and importance of this field. Section 2 details the materials and methods used to conduct the bibliometric analysis. In Section 3, the relevant literature is analyzed, identifying the main trends and directions to research in this sector. In Section 4, the results obtained and relevant discussions regarding the evolution of research and the impact of technological progress in agriculture are presented. Finally, Section 5 contains the conclusions of the study, highlighting the main findings as well as the future research directions in this field.

2. Materials and Methods

Using the specialized software VOSviewer (version 1.6.18), we performed a bibliometric analysis. VOSviewer is a computer program designed for creating, visualizing, and exploring maps based on network data [44]. According to the VOSviewer Manual, this software allows for the construction of maps either directly, from the adjacency matrix of a network, or through relationships such as co-authorship, co-appearance, citation, bibliographic linkage, and co-citation [45]. These maps can represent scientific publications, journals, researchers, researchers, organizations, countries, or keywords, with data retrieved from databases such as Web of Science, Scopus, PubMed or RIS files.

With this method, keywords, authors, institutions, and countries can be analyzed, facilitating the identification of the most frequently used concepts, the most cited authors, and the main research centers [46,47]. In addition, bibliometric analysis provides information about the impact of a publication among scholars and assesses the reputation of its authors [48]. As emphasized by Zupic and Čater [49], this method supports conducting systematic literature reviews, directing researchers to influential studies and facilitating the objective mapping of a research field.

Bibliometric analysis is an essential method for understanding the evolution and trends in a research field. According to a study by Aria and Cuccurullo [50], bibliometric analysis is a valuable tool in assessing scientific influence, identifying networks of researchers and international collaborations, and mapping the evolution of research topics over time. It helps to highlight the main emerging concepts and to analyze how knowledge is distributed within a scientific community.

The online platform Bibliometric.com was used to analyze international collaborations [51]. This application allows users to upload files exported from Scopus or Web of Science databases and automatically generates network maps that highlight links between countries, authors, or institutions.

The first step (Figure 1) in the bibliometric analysis was to identify the relevant papers. The Web of Science database was searched using only the phrase “technological progress in agriculture”, applied to the “Topic” field (covering the title, abstract and keywords of the articles). Boolean operators (AND, OR) or synonymous and complementary terms were not used, as this methodological choice was made to maintain a rigorous and focused selection of the literature. Using this approach, only papers that directly addressed the topic of technological progress in agriculture were selected, avoiding articles with marginal links or adjacent topics. The exclusion of combinations of terms contributed to a uniform and conceptually well-delimited dataset. This reduced the risk of including irrelevant publications or those which would be difficult to interpret in the context of this research. In addition, this method ensured greater clarity in analyzing trends and directions of development in the field.

Figure 2 shows the PRISMA 2020 diagram for the selection process of articles for this bibliometric analysis on technological progress in agriculture.

Of the 2271 total articles identified in the Web of Science database, 504 were automatically eliminated as ineligible and 1767 were screened, resulting in 962 articles relevant for analysis. There were no further exclusions in the final stages, indicating rigorous and consistent filtering from the initial stages of selection.

In the next step, filters were applied to restrict the results to articles in the field of agriculture. To ensure the relevance and consistency of the bibliometric analysis, only thematic categories that reflected the interdisciplinary nature of technological progress in agriculture from Web of Science were selected, integrating both environmental science and economic perspectives as well as contributions from the fields of engineering, agricultural policy, and plant science. A total of 27 categories were selected on Web of Science (Figure 3), including fields such as “Environmental Sciences” (163 articles), “Economics” (138 articles), “Environmental Studies” (84 articles), “Green Sustainable Science Technology” (78 articles), “Agronomy” (68 articles), and “Agricultural Economics Policy” (54 articles).

After applying the filters, the relevant data were downloaded in the tab delimited file format, a format compatible with specialized bibliometric analysis software such as VOSviewer. In terms of the document types (Figure 4) included in the bibliometric analysis, 487 articles (69%), 98 conference papers (14%), 94 reviews (14%), 10 early access papers (2%), 7 book chapters (1%), 2 editorial materials, 2 retracted publications, 1 book, and 1 letter were identified.

These documents were essential for analyzing trends and identifying the most relevant papers in the field. For this bibliometric analysis, it is important to note that no filter was applied for a specific time period. Therefore, the documents were analyzed across the entire range, from 1979 to 2025.

Next, several bibliometric maps were generated using VOSviewer software (version 1.6.18) in order to visualize the relationships between key concepts and collaboration networks in the analyzed literature. The keyword map was constructed based on a co-occurrence analysis of the authors’ terms using the association strength normalization method, with a minimum threshold of 5 occurrences for the inclusion of a keyword. The co-author map was created based on co-authorship analysis, revealing the structure of collaboration networks between researchers. The institutional map used institutional co-occurrence analysis, identifying connections between universities and research centers active in the field. Finally, the country map was obtained by analyzing international collaborations based on author affiliations, providing a geographical perspective on the distribution and density of research.

The final step of the bibliometric analysis was to interpret the maps. This involved identifying the main research trends, influential authors, and institutions, as well as international collaborations and the geographical distribution of research. At this stage, the correlation between the keywords and the evolution of the researched topic was explored, providing a solid basis for formulating conclusions and future research directions in the field of technological progress in agriculture.

3. Literature as a Battlefield: Diverging Visions and Hidden Assumptions

This detailed literature review on technological progress in agriculture has the aim of identifying the main directions of development in this field. Thus, the study addresses five fundamental areas: the use of digital technologies in agriculture, the adaptation of agricultural practices to climate change, robotic and automated solutions in agriculture, biotechnological advances and genetic innovations, and the identification of research gaps in the field of agricultural technological progress. These themes are key to understanding how new technologies are influencing transformations in the agricultural sector and guiding future research directions.

3.1. Digital Technologies in Agriculture

The evolution of agriculture can be categorized into five technological generations (Figure 5). Agriculture 1.0 marked the era of mechanization, introducing steam power in order to reduce manual labor. Agriculture 2.0 brought mass production through the use of tractors and electricity, enabling large-scale farming. With Agriculture 3.0, automation emerged via computers and sensors, enhancing precision in agricultural processes. Agriculture 4.0 advanced this further through digitization, integrating data and the Internet of Things for real-time monitoring. Finally, Agriculture 5.0 represents the rise of smart farming, leveraging AI, robotics, and biotechnology for sustainable, autonomous agricultural systems.

Digital agriculture, an integral part of “Agriculture 4.0”, involves the extensive use of digital technologies, such as the Internet of Things (IoT), artificial intelligence (AI), sensor systems, and data platforms, to improve the efficiency and accuracy of agricultural processes [52,53]. These technologies enable the real-time monitoring of crops and environmental factors, optimizing input consumption and automating decision-making processes, resulting in increased productivity and reduced losses [54]. Governments are promoting agricultural digitization to address major challenges such as climate change and labor shortages, and research confirms significant economic benefits, including reduced production costs and increased yields [55,56,57,58]. For example, in China, the adoption of digital technologies has led to an increase of about 30% in economic benefits for each unit of increased digitization [55].

In addition to economic benefits, digital innovations also support sustainability goals by helping to increase the productivity and reduce the environmental footprint of farms [59]. However, the transition to digital agriculture faces numerous challenges, including high implementation costs, insufficient digital infrastructure, and uncertainty about these technologies’ cost-effectiveness, which limit their adoption in developing regions [60,61]. Some smallholder farmers also face a significant digital divide, manifested through a lack of access to equipment, the internet, and adequate training, which can exacerbate economic and social inequalities [62].

In the European Union, the agricultural digitalization strategy emphasizes the integration of digital technologies not only to improve economic efficiency but also to promote social inclusion and climate neutrality [62,63]. Public policies, including the new Common Agricultural Policy (CAP) 2023–2027, support investment in rural infrastructure, lifelong learning, and process standardization, with the aim of boosting the digital transformation of the agricultural sector and making it more competitive and sustainable [64]. Digital agriculture and emerging technologies, such as precision farming and the use of data and the blockchain for traceability, have significant potential to transform the way agriculture is produced, but their successful implementation depends on overcoming access barriers and public policy support that facilitates widespread adoption [65,66].

3.2. Adjusting Agricultural Practices to Climate Change

Climate change affects agricultural sustainability by intensifying extreme weather events and increasing the risks associated with crop-specific pests and diseases. These impacts lead to significant fluctuations in productivity and economic instability for agricultural producers [67]. The agricultural sector is also a significant contributor to greenhouse gas emissions, accounting for about 11% of total EU emissions, mainly through methane from livestock and nitrous oxide from soil fertilization [68].

Integrating the principles of Climate-Smart Agriculture is a strategic direction for optimizing the sustainability of the sector. This approach simultaneously aims to increase productivity, build resilience to climate stresses, and reduce greenhouse gas emissions by implementing advanced agricultural technologies and practices [69]. Among the solutions identified are the adoption of crop varieties adapted to water- and heat-stress conditions, improved irrigation technologies, optimized fertilization through the use of controlled-release fertilizers, and the promotion of agro-ecological systems based on crop diversification and integration with livestock [70,71].

In this context, European policies play a key role in guiding the transition towards sustainable agriculture. The “Farm to Fork” strategy proposes reducing pesticide use by 50% and increasing the area dedicated to organic farming to 25% of all agricultural land by 2030, while the Common Agricultural Policy (CAP) 2023–2027 foresees allocating around 35% of its funds to measures to combat climate change and protect the environment [68], including by supporting farmers implementing sustainable practices such as conservation farming and soil carbon management [72].

The adoption of emerging technologies, such as digital soil monitoring systems and the use of artificial intelligence to optimize agricultural resources, provides additional opportunities for adapting to climate variability [67]. In this regard, the implementation of integrated policies and evidence-based solutions is essential to accelerating the transition towards a resilient and efficient agricultural model. Transforming agri-food systems in a sustainable direction is a prerequisite for achieving the emission reduction targets set by the Paris Agreement and for ensuring long-term global food security [68].

3.3. Robotic and Automated Solutions in Agriculture

Technological developments have significantly transformed the agricultural sector through the progressive integration of automated and robotized solutions [73,74]. If in the 20th century mechanization reduced physical labor intensity and increased productivity through the use of tractors and agricultural machinery, the beginning of the 21st century marks a new stage of development, with the deployment of autonomous agricultural robots [75], aerial monitoring drones [76], GPS-guided intelligent machinery [77], and automated feeding and milking systems in livestock farms [78]. These technologies contribute to increasing the efficiency, productivity, and sustainability of agriculture, optimizing the use of resources, and reducing negative environmental impacts.

According to an FAO report [79], agricultural automation technologies can generate multiple benefits, including increased labor productivity; improved working conditions, by reducing repetitive tasks and physical effort; and economic opportunities in rural areas, through the development of related industries dedicated to the manufacture, maintenance, and operation of automated equipment. Automation also helps reduce post-harvest losses and improve food quality and safety by applying precise harvesting and processing processes [80]. On the environmental side, new technologies allow for the more efficient use of agricultural resources through the precise application of inputs (e.g., fertilizers and pesticides) and the implementation of smart irrigation systems capable of efficiently managing water consumption in the context of climate change [81].

A recent study by Yépez-Ponce et al. [74] highlights emerging trends in mobile agricultural robotics, such as the development of autonomous weeding and hoeing robots that reduce the need for herbicide use, or robots equipped with computer vision systems capable of harvesting fruit at optimal maturity. The integration of the IoT and artificial intelligence into these robotic platforms enables their autonomous navigation, real-time decision-making, and optimization of key agricultural processes such as sowing, harvesting, and crop monitoring [35]. Case studies from various agricultural sectors confirm the effectiveness of these solutions, demonstrating reduced operational costs and increased yields. For example, in viticulture, the deployment of autonomous vehicles has led to a significant reduction in soil compaction and an acceleration of agricultural processes [62,82].

Despite its obvious advantages, automation also poses a number of economic and social challenges. One of the main concerns relates to the risk of human labor substitution, particularly in activities involving repetitive operations, such as harvesting and packaging [83]. This technological transition may contribute to widening economic disparities, favoring large farms, which have the capital to invest in advanced technologies, at the expense of small farms, which may have difficulties in accessing and implementing these solutions [84]. The FAO [79] emphasizes the need for proactive policies to prevent such economic polarization by promoting small-scale and affordable automated equipment, as well as by implementing training and retraining programs for the rural workforce so that workers can operate and maintain new technological systems.

Agricultural automation and robotics represent a major step towards the modernization of the agricultural sector, with the potential to significantly increase productivity and sustainability. However, the successful adoption of these technologies depends on the development of an appropriate regulatory framework that ensures the equitable distribution of technological benefits and supports the socioeconomic inclusion of all actors involved in the agri-food chain [85,86,87,88].

3.4. Biotechnological Advances and Genetic Innovations in Agriculture

Advances in biotechnology have opened up unprecedented opportunities for the genetic improvement of plants and animals in order to increase agricultural yields and resilience to stressors. Classic genetic engineering (genetically modified organisms—GMOs) is now complemented by new genomic techniques (NGTs) such as CRISPR-Cas9, which allows much more precise and faster genetic editing of plant genomes [89,90]. These revolutionary techniques offer researchers the possibility of developing crops with improved traits—enhanced drought, heat, or salinity tolerance, better disease and pest resistance, increased nutrient utilization efficiency, and even nutrient fortification (biofortification)—in a much shorter time frame than conventional methods [91]. Akanmu et al. [90] highlight the role of CRISPR technology in ensuring food security: remarkable progress has already been made in creating drought- and disease-resistant plant varieties with increased yields and improved nutritional quality, demonstrating that genome editing can be a central tool for sustainable agriculture [92]. For example, using CRISPR-Cas9, researchers have produced high-temperature-tolerant rice; wheat, potato, and banana varieties resistant to devastating viruses; and antioxidant-enriched tomatoes (e.g., with high lycopene levels) [93]. Many of these innovations are still in the testing phase, but some have already reached the market: for example, tomatoes rich in GABA (a health-promoting amino acid) obtained by gene editing have been commercialized in Japan, and in the US there are already more than 20 genome-edited (non-GMO) crops approved for cultivation, such as non-browning mushrooms, soybeans with improved oleic profiles, and high-yielding corn [94,95].

Conventional biotechnology has also contributed substantially to global production growth in recent decades. Worldwide, more than 190 million hectares are cultivated annually with GM crops (soy, maize, cotton, oilseed rape), which has led to yield increases and the reduced use of chemical pesticides in many regions [96]. For example, the widespread adoption of insect-resistant genetically modified cotton has reduced pest losses and decreased the need for insecticides, improving both farmer profitability and environmental health [97].

In addition to crops, biotechnology also brings innovations in animal husbandry (e.g., genetic markers for the selection of animals with better forage efficiency or disease resistance) and food (microbial fermentation, alternative proteins) [98]. Precision fermentation allows for the production of proteins identical to those in milk or eggs using modified microorganisms, offering potential for alternative food products with lower climate impacts [99]. At the same time, cloning and transgenesis techniques in animals aim to produce breeds resistant to diseases such as avian influenza or African swine fever [100]. These developments also raise ethical and public acceptance questions, but scientific advances point to their substantial benefits: Akanmu et al. [90] conclude that, despite controversies about the safety of the new biotechnologies, their importance is already evident in creating drought- and disease-resistant crop varieties and improving yields and food quality [101,102].

3.5. Research Gaps in Technological Progress in Agriculture

Gaps in the literature highlight the need for further research on the digitization of agriculture, sustainability, and the impact of emerging technologies on productivity and food security.

Regarding the adoption of digital technologies in agriculture, the literature highlights their benefits on efficiency and sustainability, but there is a lack of studies on their long-term impacts on farmers’ incomes and economic distribution across regions. Technological and economic barriers are also frequently mentioned, but practical solutions in order to overcome these obstacles are insufficiently explored. The integration of artificial intelligence and the Internet of Things is touted as a promising direction, but the implications for small and medium-sized farms are still poorly documented.

The adaptation of agriculture to climate change is another under-covered area, as regards the differences between developed and emerging regions. Existing studies focus on international policies, but detailed economic assessments of the impacts of adaptation measures, such as the use of drought-tolerant crops or smart irrigation, are lacking. Moreover, risks associated with reliance on emerging technologies, such as the cybersecurity of digitized agricultural infrastructure, remain largely unexplored.

Concerning automation and robotization in agriculture, the literature explores the economic and environmental benefits, but there are few studies on the social impacts of these technologies on rural employment. Also, current research focuses mainly on large farms without providing concrete solutions for integrating these technologies into small farms. Another significant gap is the lack of clear standards for the interoperability of automation systems and the integration of artificial intelligence into agricultural processes.

Biotechnology and genetic innovations are growing fields, but the current literature is deficient in the ethical and social implications of these technologies. Also, international regulations for new genomic techniques (NGTs) are not sufficiently explored in relation to genetically modified organisms (GMOs) and gene-edited crops. In addition, the long-term impact of biotechnology on ecosystems and human health requires further research.

To bridge these gaps, longitudinal studies on the impact of digitization and automation in agriculture, the development of sustainable models for technology adoption by small and medium-sized farms, and the exploration of strategies to secure digital agricultural infrastructure are needed. Biotechnology regulations should also be further investigated, along with mechanisms for integrating artificial intelligence and the blockchain into the traceability of agricultural products in order to ensure sustainability and food security.

4. Results and Discussion

To address RQ1, this analysis focuses on the evolution of the number of scientific publications in the field of technological progress in agriculture up to 2025. This analysis includes identifying publishing trends and the rhythmic evolution of research.

An analysis of the trend of publications (Figure 6) on technological progress in agriculture shows a significant evolution over the last two decades, reflecting a progressive increase in academic interest in this field. In the period of 2000–2010, the number of annual publications in this field remained relatively low, ranging between 8 and 49, suggesting moderate interest from the scientific community, probably driven by the incipient level of adoption of advanced technologies in agriculture. Since 2010, the number of published studies has gradually increased, reaching 129 in 2019, which can be attributed to the accelerated development of digital technologies such as artificial intelligence, the automation of agricultural processes, and the use of sensors for crop monitoring.

This upward trend has significantly accelerated since 2020, when the number of publications increased exponentially, reaching 344 in 2024. This dramatic increase suggests an intensification of research on the integration of emerging technologies into agriculture in the context of global challenges related to climate change and food security. At the same time, international initiatives to promote sustainable agriculture and the shift towards digitization-based agricultural practices have also fueled this trend.

For the year 2025, the data available after the first quarter of this year show that there have been 82 publications. This number can be explained by the fact that the actual number of publications will become clearer as new studies are indexed in academic databases. Therefore, although in the short term there is an apparent reduction in the volume of research, it is premature to conclude that there has been a reversal of the general upward trend.

4.1. Co-Compete Network Analysis of Keywords

In order to verify whether the main directions identified in the literature were also supported by bibliometric data, an analysis of the co-occurrence of keywords was performed. To answer RQ2, a keyword map analysis was performed on the relevant literature to identify the main concepts and research themes in the field of technological progress in agriculture. In order to identify the main thematic directions in the specialized literature on technological progress in agriculture, a co-occurrence analysis of keywords was performed using VOSviewer software. The generated map highlights the conceptual structure of the field, organizing 109 terms into six distinct clusters, each representing a well-defined thematic core, correlated with both research dynamics and current challenges in the agricultural sector (Figure 7 and

Table 1. Distribution of keywords according to clusters identified by bibliometric analysis.

Cluster Number	Cluster Color	Keywords
Cluster 1	Red	adoption, artificial intelligence, biodiversity, biomass, challenges, classification, conservation, consumption, design, economics, farmers, information, internet, management, mechanization, nitrogen, policy, precision agriculture, quality, security, sensors, smart agriculture, sustainability, sustainable agriculture, sustainable development, sustainable development, systems, technologies, yield
Cluster 2	Green	agricultural productivity, China, convergence, countries, data envelopment analysis, DEA, decomposition, efficiency, efficiency change, energy efficiency, impact, index, industry, input, investment, Malmquist index, output, panel-data, performance, productivity growth, progress, reforms, sector, technical change, technical efficiency, technical progress, technological change, technological-progress, total factor productivity
Cluster 3	Blue	agricultural technology, agriculture, climate change, climate-change, demand, economic growth, environment, food, food security, future, greenhouse-gas emissions, intensification, land-use, model, poverty, productivity, spatial Durbin model, technological change, united-states, water, wheat
Cluster 4	Yellow	adaptation, agricultural carbon emission, carbon emissions, CO2 emissions, cointegration, economic-growth, emissions, energy, energy-consumption, environmental Kuznets curve, impacts, innovation, renewable energy, technological innovation, world
Cluster 5	Purple	growth, labor, population, structural change, structural transformation, technological progress, technology, trade, urbanization
Cluster 6	Cyan	crops, determinants, farms

Source: Own processing in VOSviewer.

).

Cluster 1 (red) brings together terms associated with smart technologies and sustainability in agriculture, including concepts such as smart agriculture, artificial intelligence, sensors, sustainability, policy, and biomass. This cluster reflects concerns about the integration of new technologies into agricultural practices and their contribution to achieving sustainable development goals. The large number of related terms reveals a solid research direction focused on innovation and digital transformation in agriculture.

Cluster 2 (green) focuses on technical efficiency, performance evaluation, and agricultural productivity, consisting of terms such as technical efficiency, data envelopment analysis, panel data, productivity growth, Malmquist index, and output. It reflects literature oriented toward measuring efficiency and the comparative analysis of technological progress using advanced econometric methods. Visually, this cluster is one of the densest, suggesting intense activity in quantitative research.

Cluster 3 (blue) includes terms associated with climate challenges and the sustainability of agricultural systems, such as climate change, food security, greenhouse gas emissions, land-use, environment, and water. Its position at the top of the network suggests a contextual approach, in which technological progress is analyzed through the lens of its impact on the environment, food security, and climate change.

Cluster 4 (yellow) reflects research directions on energy transition and emissions reduction, with terms such as carbon emissions, renewable energy, adaptation, technological innovation, and environmental Kuznets curve. This thematic area highlights the growing interest in decarbonizing agriculture and developing innovative solutions with low environmental impact.

Cluster 5 (purple) includes terms that define structural transformations and the macroeconomic impact of technological progress, including growth, structural transformation, urbanization, labor, technology, and trade. The positioning of this cluster indicates the link between agriculture, innovation, and broad economic developments, providing a systemic perspective on the modernization of the agricultural sector.

Cluster 6 (cyan) is the smallest cluster and focuses on aspects related to agricultural production at the farm level, with terms such as crop, determinants, and farms. This area concentrates on applied approaches, focusing on optimizing agricultural production and the factors that influence yield at the microeconomic level.

The term agriculture appears at the center of the network and is visualized with the largest size, indicating its high frequency and central role in the analyzed literature. The high density of connections between terms such as efficiency, growth, climate change, and innovation highlight the interdisciplinary nature of the field, in which technology, economics, and the environment interconnect to shape new directions for research.

4.2. Author Co-Citation Network

To address RQ3, an analysis was conducted of the most published and most cited authors in the field of technological progress in agriculture. To analyze the co-occurrence network of keywords, VOSviewer software was used, applying the modularity clustering method to identify coherent thematic clusters. The visual representation highlights the significant connections between terms, and the size of the nodes reflects the frequency of occurrence of each keyword in the analyzed literature.

Within the bibliometric analysis of authors, the co-authorship network map highlights the collaborative structure of research in the field of agricultural technological progress. This network reflects the dynamics of cooperation between authors working in different fields but connected by significant scientific collaborations. The map is structured based on groups of authors, called clusters, which indicate frequent collaborations between researchers from different parts of the world and different institutions.

The co-authorship network identified in this bibliometric analysis is characterized by the existence of several clusters, each representing a group of authors who frequently collaborate with each other. These clusters are delineated by the connections and collaborations established over the course of publishing research (Figure 8). A dominant cluster, marked in red, includes the authors Liu Jianxu and Cui Jiande, who form a core group of researchers with an extensive network of collaborators, indicating outstanding scientific activity in this field. Other significant clusters include the authors Shen Zhiyang and Balezentis Tomas (yellow cluster) and Lei Xiaohui and Lyu Xiaolan (green cluster), signaling groups of researchers who are distinguished by frequent collaborations and significant results in the field of agricultural technology.

Identifying influential authors within the co-authorship network, the authors who occupy central positions are those with the most connections and collaborations with other researchers, having a significant impact on the field. Liu Jianxu, Shen Zhiyang, and Deng Yue are identified as among the most influential researchers, with an extensive network of collaborators and a consistent presence in recent research. These central figures play an important role in disseminating knowledge and stimulating the advancement of research in the field of agricultural technological progress. Influential authors are also strategic points in the co-authorship network, and their collaborations can open new directions for future research.

Analyzing dispersion and international collaboration, the co-authorship network reveals both groups of authors who collaborate intensively within their own clusters and authors who are isolated or have limited connections. This suggests concentrated collaboration among particular groups of researchers, which may imply less integration between the various sub-domains of agricultural technological progress. While some authors are involved in international and transnational collaborations, others remain more isolated, with a more restricted co-authorship network, indicating either a more independent approach to research or their low integration into mainstream research networks.

Table 2. Ranking of authors by number of documents.

Ranking	Author	Documents	Citations
1	Liu, Jianxu	5	110
2	Rahman, Sanzidur	5	238
3	Shen, Zhiyang	5	196
4	Balezentis, Tomas	3	20
5	Bravo-ureta, Boris e.	3	61
6	Chandio, Abbas Ali	3	130
7	Deng, Yue	3	59
8	Hertel, Thomas W.	3	101
9	Li, Yan	3	27
10	Mariyono, Joko	3	59

Source: Own processing in VOSviewer.

presents the authors with the highest number of publications in the field of technological advancement in agriculture, highlighting both their productivity and the impact of their work as measured by the number of citations. It is observed that three authors—Liu Jianxu, Rahman Sanzidur, and Shen Zhiyang—have five publications each, suggesting a significant contribution to the advancement of knowledge in this field. Among them, Rahman Sanzidur stands out with the highest number of citations (238), indicating a considerable influence on the scientific community.

The citations (Figure 9) were analyzed using the citation analysis function in VOSviewer, which allows for the identification of high-impact authors based on their total number of citations and highlights their position in the network in relation to several thematic clusters, reflecting their transdisciplinary influence in the field of research. Seven authors have three publications each, but their impacts vary significantly. Abbas Ali Chandio and Thomas W. Hertel both have a significant number of citations (130 and 101, respectively), reflecting a growing recognition of their contributions to agricultural research. On the other hand, Tomas Balezentis and Li Yan have lower citation values (20 and 27, respectively), which may indicate either the lower visibility of their work or their operation within a niche field with a narrower impact.

Figure 9 illustrates the authors with the highest number of citations and connections, highlighting the structure of the academic network by forming seven distinct clusters. The red cluster is dominated by Liu Jianxu, while the yellow cluster includes the authors Shah Wasi Ul Hassan and Hamid Salman. The blue cluster comprises Bravo Boris E., Thirtle C. and Nkmaleu Gb, and the purple cluster Shen Zhiyang. The light blue cluster is centered around Deng Yue, while the green cluster, characterized by a significant number of connections and relationships, includes authors such as Gong Binlei, Irz X, Hu Jiangfeng, Ito Junichi, and Chandio, Abbas Ali. The positioning of the green cluster at the extreme end of the graph suggests that the authors in this cluster address distinct research topics from other authors who are concentrated in the central area of the network, indicating a higher degree of thematic integration.

The citation network emphasizes the existence of well-defined structures, where a group of authors is frequently cited by other researchers working in similar fields, such as the authors who formed the green, blue, and red clusters. This pattern suggests the existence of stable research nuclei, where the most influential authors contribute to strengthening the theoretical and methodological foundations of the field. At the same time, the analysis reveals several distinct clusters, each corresponding to specific research directions, such as the light blue cluster represented by Deng Yue and the yellow cluster represented by Shah Wassi Ul Hassan and Hamid Saiman. These clusters show that the reviewed literature is organized into relatively well-demarcated sub-domains which are interconnected by works with transdisciplinary impact.

The bibliometric analysis of citations reveals two distinct typologies of influential authors: authors with a high number of citations and extensive connections, who are reference points in the scientific network and have an impact on several sub-domains, and authors with a high number of citations but fewer connections, who are typically researchers who have introduced innovative concepts or methodologies subsequently adopted by various scientific communities. Liu Jianxu and Bravo Boris E. are representative examples of such authors, having influenced a wide area of research.

Citation trends indicate that authors with a high number of citations tend to relate to multiple groups, suggesting that their work has a transdisciplinary influence. This is important in bibliometric analysis as it highlights how particular studies contribute to the development of the field, serving as bridges between different sub-branches of research.

The most cited authors represent an important role in the theoretical and methodological development of the field of agricultural technological progress. The frequent citation of certain works suggests that they serve as a conceptual basis for further research, strengthening the foundations of the field and providing directions for new investigations. Thus, their studies not only reflect the current state of knowledge but also chart future perspectives for the field.

In terms of the distribution of influence, authors with many connections can be seen as catalysts for collaborations and the exchange of ideas between different subfields. Conversely, those with many citations but fewer direct connections can be seen as pioneers of revolutionary concepts, whose work is subsequently integrated and developed by wider scientific communities.

4.3. Collaborative Institutional Analysis of Co-Authors

To answer RQ4, the distribution of research in technological progress in agriculture was analyzed across various academic institutions worldwide. The analysis identified the academic centers with the greatest impact on the field, highlighting institutions that have significantly contributed to advancing knowledge and developing innovative technological solutions for agriculture.

The organizational network analysis highlights the structure of institutional collaborations in the field of agricultural technological progress, reflecting the distribution and intensity of interactions between research institutions. The results obtained indicate the existence of distinct clusters, each representing consolidated collaborative networks between academic institutions and research centers.

Figure 10 shows a complex network of collaborations between institutions involved in agricultural technological progress research, highlighting the formation of eight distinct clusters. Each cluster reflects a group of interconnected institutions with a significant impact on research and development in the field, and their distribution on the organizational map suggests the geographical and specialization relationships between the institutions.

The first cluster, marked in red, is represented by Wageningen University, a Dutch institution globally recognized for innovations in agriculture and technology. It plays a central role in the network, being an important hub for international collaboration and a leader in global agricultural research. Further, the orange cluster includes the Beijing Institute of Technology, an institution in China which is involved in the development of advanced technological solutions applicable to agriculture, emphasizing China’s significant contributions in agricultural technological advancement and.

Another significant cluster is the purple cluster, which includes institutions from various regions: the Lithuanian Center for Social Sciences, Nanjing Agricultural University, the University of Sharjah, and Sichuan Agricultural University. These institutions are in different corners of the world, reflecting the geographical diversity of international collaboration. Representing a mix of European, Asian, and Arab research institutions, this cluster illustrates the globalization of agricultural research, with a focus on technological solutions that can be adapted to different economic and social contexts.

The light blue cluster is represented by the Chinese Academy of Sciences (CAS), one of the most influential institutions in China with an extensive network of domestic and international collaborations. CAS plays an important role in the coordination of research and the application of advanced agricultural technologies, with multiple connections in Asia and other regions, underlining its centrality in the field of agricultural technological advancement.

Next, the yellow cluster includes Northeast A&F University, and the blue cluster represents China Agricultural University. Both of these Chinese institutions are actively involved in research and development related to sustainable agriculture and agricultural technological advancement. China Agricultural University, in particular, is an important center of excellence in this field, with multiple collaborations with national and international institutions.

The last cluster, represented by Shandong University of Finance and Economics, completes the network by adding an institution that focuses on the study of the economic impact of agricultural technological progress. This institution brings a valuable perspective on the financial and economic policy impacts of agricultural policies and is an important player in the assessment and implementation of innovative technologies in agriculture.

Thus, an analysis of the regions to which these institutions belong emphasizes a global network of collaboration, with a strong concentration in China but also significant contributions from Europe and the Middle East. This geographic and thematic diversity of institutions suggests an intense exchange of knowledge and innovation, with the aim of integrating advanced technologies in agriculture to develop a more sustainable and efficient agricultural sector.

Table 3. Top 10 universities by number of affiliated publications published in Web of Science database.

Ranking	Organization	Documents	Citations
1	Chinese Academy of Sciences	13	131
2	Beijing Institute of Technology	10	284
3	Northwest A&F University	9	155
4	Sichuan Agricultural University	9	275
5	Huazhong Agricultural University	8	227
6	Nanjing Agricultural University	7	40
7	China Agricultural University	6	108
8	Shandong University of Finance and Economics	6	127
9	University of Chinese Academy of Sciences	6	54
10	Chiang Mai University	5	110

Source: Own processing. Data processed in the VOSviewer program.

also shows the top 10 institutions with the highest number of documents in the field of technological progress in agriculture. The results indicate a varied distribution of the number of publications and their impact as measured by the number of citations.

Also, some universities mentioned in Table 3 are not represented in Figure 10, despite their significant number of published papers. This is because, although these institutions have made considerable contributions to the field, they have not established sufficiently strong collaborative links with other institutions. Thus, the lack of significant connections prevents them from being included in the collaborative network.

The institutions with the highest scientific output are the Chinese Academy of Sciences (CAS), with a total of 13 papers, followed by the Beijing Institute of Technology (BIT) with 10 publications and Northwest A&F University and Sichuan Agricultural University, each with 9 papers. However, citation analysis shows that a higher number of publications does not automatically guarantee a higher scientific impact. For example, the Beijing Institute of Technology has the highest number of citations (284), followed by Sichuan Agricultural University (275), suggesting that the research of these institutions is particularly influential in the scientific community.

Another institution with a notable impact is Huazhong Agricultural University, which, despite having only eight publications, has accumulated a considerable number of citations (227). This indicates that the studies published by this institution are of high relevance and are frequently used in the literature. In contrast, Nanjing Agricultural University has seven publications but a significantly lower number of citations (40), which may suggest either the lower visibility of its research or an orientation towards less explored sub-domains across the international community.

When analyzing institutions with a lower number of publications, significant differences in their impact can be observed. For example, China Agricultural University and Shandong University of Finance and Economics have six published papers each but have 108 and 127 citations, respectively, suggesting good responses to their work. In contrast, the University of Chinese Academy of Sciences has a similar number of publications (six) but a lower impact (54 citations), which may indicate a need to increase the visibility of the research carried out at this institution.

4.4. Country-Level Research Analysis and Collaboration

To address RQ5, the contribution of key countries to research in digitized and sustainable agriculture was analyzed, identifying the most active countries in this sector. Additionally, the structure of international collaborations was examined, highlighting strategic partnerships between countries and their impact on global technological progress in agriculture.

The global research network in agricultural technological progress has a clearly defined structure, with key countries located in various clusters, each with a specific role in international collaborations. The main cluster, represented by China (brown), is dominant, with the most international connections, including collaborations with Lithuania, Iran, and Canada (Figure 11). This core cluster reflects China’s strong influence in agricultural technologies, being a key player in both bilateral collaborations and global partnerships.

At the far left is the orange cluster, representing Russia, Ghana, India, and Nigeria, which has a limited number of collaborations but significant potential for development. This cluster is on the periphery of the network due to its research themes.

On the eastern side of the network, other significant clusters include the red cluster (Turkey, Ethiopia, Japan), which suggests a concentration of collaborations between Asian and Middle Eastern countries, and the green cluster (USA, Australia, Germany, Finland, Australia) emphasizing the role of these highly interconnected nations in agricultural technology.

On the western side of the network, the blue cluster, which includes Spain, Brazil, Taiwan, and Mexico, reflects an active area of agricultural research, particularly in Latin America and Asia. Nearby, the light blue cluster, which includes Greece, England, and South Africa, suggests a collaborative dynamic focused on agricultural innovations in Europe and Africa.

The purple cluster, representing Norway and Sweden, emphasizes research on agricultural technologies in the Nordic regions, and the yellow cluster, with Poland, Qatar, and Switzerland, completes the network, indicating a small but important amount of collaboration between these countries.

Figure 12 illustrates a complex network of international connections between countries involved in research on technological progress in agriculture, based on data extracted from the Web of Science database for the period 1979–2025. This map was generated by analyzing co-authorship at the country level, highlighting scientific collaboration links between states and emphasizing global interdependence in a field that is of strategic importance for food security and sustainability.

We observe that China plays a central role in this network, being a major hub of scientific cooperation, as confirmed by the significant size of its segment. This can be explained by China’s substantial investments in digitized agriculture, biotechnology, and automation, as well as its collaborative research programs with multiple countries.

The United States and Australia are also important hubs in this network, supporting extensive collaborations with various countries in Europe, Asia, and Latin America. These countries play an important role in the development of emerging technologies, including the use of artificial intelligence in precision agriculture and the integration of sensors for crop optimization.

Europe shows a fragmented but active distribution, with numerous connections between countries such as Germany, France, the Netherlands, and the UK. This suggests the existence of regional research networks aimed at implementing sustainable technological solutions tailored to European agricultural specificities. Similarly, India and Brazil demonstrate significant involvement, strengthening partnerships with both developed and developing countries, underlining their emerging role in agricultural innovation.

Therefore, the structure of the map indicates a clear trend of the globalization of research in agricultural technological advancement, where countries with advanced innovation capabilities are supporting extensive partnerships with emerging economies. This dynamic reflects both the need for knowledge and technology transfer and the importance of international cooperation in the face of the challenges of climate change, food security, and the sustainability of agricultural production.

Moreover, the intensity of international collaborations shown in the network indicates that scientific productivity in the field of agricultural technological progress is no longer concentrated within isolated national systems but increasingly integrated into multilateral platforms and cross-border research consortia. This shift promotes the standardization of methodologies, enhances data interoperability, and accelerates the dissemination of context-adapted innovations. The map also highlights notable collaborations between emerging economies, especially involving countries with expanding research capacities such as Brazil, India, and South Africa, suggesting a diversification of leadership in agricultural innovation beyond traditional global powers. Such dynamics are essential for building resilient and inclusive agri-food systems capable of facing future disruptions.

5. Conclusions

This study aimed to explore, through a bibliometric approach, the research trends, thematic structures, and scientific collaboration networks in the field of technological progress in agriculture. The results reveal a growing academic interest in agricultural digitization, artificial intelligence integration, and sustainable practices, offering a structured basis for future research directions.

In relation to RQ1, concerning the trend in the publication of scientific articles on technological progress in agriculture, the results indicate a steady increase in the number of publications since 2010 and a significant acceleration after 2020, reflecting the scientific community’s growing interest in innovative solutions in the context of climate change and the transition to sustainability. The analysis associated with RQ2, on recurring concepts and themes in the literature, highlighted six major thematic clusters, which underscore the interdisciplinary nature of the field and the convergence between digitization, sustainability, agricultural productivity, climate policies, and economic transformations. With regard to RQ3, related to the identification of the most influential authors, the analysis of the co-authorship network highlighted researchers such as Liu Jianxu, Rahman Sanzidur, and Shen Zhiyang, who have high visibilities and central roles in the thematic development of the field. Answering RQ4, which focuses on the institutions with the most relevant scientific contributions, the most active centers come from China, particularly the Chinese Academy of Sciences, where the impact of research is strongly correlated with the degree of international collaboration. Finally, for RQ5, regarding countries that contribute significantly to the literature, the data show that China dominates both in terms of publication volume and influence in global networks, followed by the United States, Australia, India, and several European countries, while emerging countries in Africa and Latin America are gradually consolidating their presence in these international scientific networks’.

Based on the results obtained, it is important to note that technological progress in agriculture offers numerous opportunities for scientific advancement, and future research directions should reflect the complexity and dynamics of this field. The first direction concerns investigating the practical applicability of emerging technologies, such as artificial intelligence, the Internet of Things, and automation, in different agricultural systems, with a focus on adaptability and efficiency. The second direction includes analyzing the social and economic impact of digitization in agriculture, especially in rural areas and among small farms, to assess the level of technological inclusion. Finally, a third direction concerns the integration of sustainability into agricultural performance assessment by developing analytical frameworks that correlate technological progress with reduced environmental impact and green policy objectives. These directions are essential for the development of smart, fair, and sustainable agriculture in line with the challenges of the future.

We first and foremost recommend, for policymakers, the implementation of government policies that support research and development in digital agriculture. These policies should encourage investment in advanced technologies such as artificial intelligence and automation, and by allocating funds for innovation projects the transition to more efficient and environmentally friendly agriculture can also be incentivized. In addition, support through grants and tax incentives will facilitate the rapid transfer of knowledge and technologies to farmers in less-developed regions, helping to bridge the technology gap between countries.

Second, sustainability policies must become a central pillar of agricultural regulation, aiming to reduce negative environmental impacts and promote a circular economy in agriculture. These policies should include clear measures to reduce greenhouse gas emissions and optimize the use of natural resources such as water and energy. At the same time, the promotion of environmentally friendly farming practices and regulations imposing stricter standards for waste management will create a favorable framework for the adoption of technologies that contribute to a healthier and more sustainable agricultural environment.

This study’s contribution is twofold: it is conceptual, by clearly outlining the main research directions, and methodological, by using bibliometric tools to map scientific developments. Thus, the research provides valuable insights for developing the future agenda and supports informed decision-making by those involved in the digitization of agriculture.

The limitations of the paper are related to the exclusive use of the Web of Science database and the focus on English-language literature, which may exclude relevant contributions from other sources. In addition, the interpretation of bibliometric networks depends on the consistency of the metadata entered by authors, which may affect the overall accuracy of the analysis.

In conclusion, technological progress in agriculture is emerging as a dynamic area with major implications for the sustainability and competitiveness of the agricultural sector. The integration of emerging technologies contributes not only to increasing efficiency and productivity but also to adapting to new climatic and economic challenges. The current research reflects a clear move towards the digitization of agriculture, but also a strong need to develop coherent policies to support this transition. Strengthening international collaborative networks and promoting transdisciplinary approaches will be key to accelerating innovation in this area. In this context, future research directions should aim at the practical applicability of technological solutions, reductions in regional disparities, and the creation of a favorable framework for sustainable, smart, and equitable agriculture.