Mapping the Evolution of Artificial Intelligence in Agriculture: A Large-Scale BERTopic Analysis of Smart Farming, Automation, and Precision Systems (2020–2025)
A descriptive bibliometric and thematic synthesis of 31,452 publications on the application of artificial intelligence in agriculture in Scopus from 2020 to 2025 reveals a concentrated research landscape dominated by production-oriented applications, while interdisciplinary and emerging AI domains remain relatively fragmented.
Abstract
Advances in artificial intelligence (AI) are revolutionizing agriculture through applications in crop monitoring, precision agriculture, automation, and environmental management. With the rapid development of AI in agriculture, there is an increasing need for extensive evaluation to identify emerging trends and potential future directions. This paper provides a descriptive bibliometric and thematic synthesis of 31,452 publications on the application of artificial intelligence in agriculture in Scopus from 2020 to 2025. The trends and structure of topics in the area are analyzed using BERTopic topic modeling alongside thematic synthesis, temporal trend analysis, centrality-density mapping, and evidence synthesis. Six higher-order themes were identified in the analysis, with crop and production intelligence being the most common. Despite the current progress, the topic of crop-related applications of computer vision remains dominant in the research landscape. However, applications in livestock, socio-technical systems, sustainability, and advanced distributed artificial intelligence fall at the periphery of the landscape. Temporal and structural analyses reveal a concentrated research landscape dominated by production-oriented applications, while interdisciplinary and emerging AI domains remain relatively fragmented. The paper also emphasizes the importance of combining transformer-based topic modeling with thematic and structural analysis in multidisciplinary fields. The results have revealed important insights into the direction AI technology is taking in the agricultural sector, underscoring the need for interoperable, explainable, sustainable, and farmer-centered systems for agricultural development.
Precision agriculture is a technological framework that applies various information and communication technologies, such as remote sensing and Internet of Things (IoT), to agriculture. In recent years, advances in artificial intelligence and robotics technologies have led to the widespread adoption of precision agriculture (i.e., smart agriculture), which is expected to improve productivity and reduce labor, making it an important technology for enhancing the sustainability of agriculture in the future. Therefore, in this study, we aimed to examine the research trends related to precision agriculture through topic-based bibliometric analysis. Additionally, we performed dynamic topic modeling to examine the evolution of research trends over time. We used BERTopic, which has become increasingly popular in recent years, as the topic modeling implementation tool. The data analyzed were the title texts of academic papers related to precision agriculture obtained from the OpenAlex academic literature database. By applying the topic modeling tool BERTopic to these title texts, we extracted a variety of topics from previous research on precision agriculture, including research on unmanned aerial vehicles (UAVs) and the IoT. Furthermore, application of the dynamic topic model revealed temporal changes in topics, indicating that research on disease detection using deep learning has become increasingly active in recent years.
Kosuke Kato, Y. Uenishi· IOP Conference Series: Earth...· 0 citations
It is argued that AI-ML integration improves productivity in agriculture in terms of crop yield prediction, disease prediction and optimization of resources, amongst others, and a comprehensive strategy for future work in designing sustainable agrifood systems is proposed.
Anita Veerappa Karkikatti, R. H. Goudar, Vijayalaxmi N. Rathod et al.· Discover Artificial Intellig...· 0 citations
Artificial intelligence (AI) is increasingly applied in agriculture to support data-driven decision-making, improve productivity, and enhance resource management. Small-scale farmers, who produce a significant share of the world’s food yet often operate under resource constraints, may particularly benefit from these technologies. However, it remains unclear how AI research addresses the needs of small-scale farming systems and the extent to which farmers directly interact with AI tools. This study conducts a systematic literature review to examine the applications, impacts, and challenges of AI in small-scale agriculture. The review followed the PRISMA 2020 guidelines and applied a structured review methodology, using the Web of Science, Scopus, and EBSCOhost databases. A total of 182 studies were identified and analyzed. The results show a rapid increase in publications after 2020, with research concentrated mainly in Africa and Asia. Most studies focus on technical AI applications such as plant disease detection, crop yield prediction, crop classification, and environmental monitoring, commonly using machine learning and deep learning techniques. However, only a small number of studies examine farmers’ direct interaction with AI systems, including adoption, perceptions, and practical usage. This imbalance indicates that the literature remains largely technology-driven rather than farmer-centred. The review highlights important research gaps, particularly in farmer engagement, integrated farm management applications, and the translation of AI prototypes into scalable solutions. Future research should prioritize participatory approaches and context-sensitive AI systems to ensure that technological advances effectively support small-scale farmers and sustainable agricultural development.
Zimbini Coka, M. Monteiro, B. Jammer· Agriculture· 0 citations
Climate change is increasingly disrupting agricultural systems worldwide through more frequent extreme
weather events, shifting rainfall patterns, and rising temperatures. These changes contribute to reduced crop yields,
unstable food supplies, and heightened production risks, especially in vulnerable developing regions. In response, ClimateSmart Agriculture (CSA) has been developed as a strategic framework to improve agricultural productivity while
enhancing resilience and promoting environmentally sustainable farming practices. Within this framework, Artificial
Intelligence (AI) is increasingly recognized as a transformative tool capable of supporting data-driven agricultural
decision-making. This review systematically synthesizes recent literature on AI applications in CSA, focusing on four key
thematic areas: crop yield prediction, weather and climate forecasting, irrigation and water management, and pest and
disease detection under climate stress. Peer-reviewed articles published between 2020 and 2026 were retrieved from
Google Scholar, Semantic Scholar, and Crossref. A total of 170 studies were initially identified, of which 58 highly relevant
articles were selected following screening based on relevance, quality, and thematic alignment. Findings indicate that AIbased systems significantly improve predictive accuracy, optimize resource use, and enhance early warning capabilities
across agricultural systems. Machine learning, deep learning, remote sensing, IoT, and big data analytics are widely
applied to support precision agriculture and climate adaptation strategies. However, key challenges such as data scarcity
in developing regions, high implementation costs, limited farmer adoption, and climate uncertainty continue to constrain
widespread adoption. The review concludes that AI holds strong potential to transform CSA by improving productivity,
resilience, and sustainability. Nonetheless, its effectiveness depends on the development of accessible, affordable, and
context-specific solutions supported by strong policy frameworks and improved digital infrastructure.
Sonny Gad Attipoe· International Journal of Inn...· 0 citations
This study provides an extensive overview of AIoT research in smart farming and provides useful directions for researchers and practitioners interested in the future development of digital agriculture.
Purpose: Achieving global food security while maintaining environmentally sustainable agricultural systems remains a critical challenge amid population growth, climate variability, and resource constraints. Artificial Intelligence (AI) and the Internet of Things (IoT) have emerged as transformative technologies that support data-driven agricultural practices. This study systematically examines the applications, opportunities, challenges, and adoption factors of AI-IoT integration in smart agriculture, with particular emphasis on its potential contributions to food security and sustainable farming.
Methods: A systematic literature review (SLR) was conducted following the PRISMA 2020 guidelines. Publications were retrieved from Scopus, IEEE Xplore, and ScienceDirect covering the period 2020-2024. From an initial 431 records, 17 empirical studies met the inclusion criteria and were analysed using narrative and thematic synthesis.
Result: The review shows that AI-IoT technologies are primarily applied in crop disease detection, precision agriculture, environmental monitoring, yield prediction, and livestock health monitoring. These technologies enable real-time decision support, early disease detection, productivity improvement, and resource optimisation. However, challenges remain, including data limitations, infrastructure constraints, integration complexity, and high deployment costs.
Novelty: The study proposes a layered smart agriculture framework linking technological infrastructure, application domains, adoption conditions, operational outcomes, and sustainability impacts. The findings highlight key factors necessary for successful implementation, including infrastructure readiness, affordability, technological reliability, and capacity development for farmers. Crucially, the review demonstrates that the field of AI-IoT smart agriculture is technically advanced but socio-technically incomplete: the evidence base is dominated by proof-of-concept studies from Asia, with no empirical representation from Africa or the Americas, creating what this study terms an AI-IoT agricultural equity gap that fundamentally limits the technology’s contribution to global food security. The five-layered framework introduced here provides the first inductively derived organising structure that explicitly connects AI-IoT infrastructure to SDG-aligned food security outcomes, offering a replicable analytical scaffold for future empirical and policy research in this domain.
B. Ndlovu, Kudakwashe Maguraushe· Scientific Journal of Inform...· 0 citations