Aug 2026· Sustainability· Vol 18, pp. 8073· 0 citations· 118 references
TL;DR
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.
Abstract
Artificial Intelligence of Things (AIoT), which refers to the combination of Artificial Intelligence (AI) and the Internet of Things (IoT), has become an important research area in smart farming. Although the literature on AIoT has expanded rapidly, little is known about its overall development, intellectual structure, and emerging research directions. To bridge this research gap, this paper examines the AIoT literature in smart farming using a bibliometric approach based on publications indexed in the Web of Science Core Collection. Bibliometric techniques, including performance analysis, keyword co-occurrence analysis, and bibliographic coupling, were employed to examine publication trends, most relevant journals in the field, top scholars, countries, institutions, and research themes. The findings show a rapid growth of AIoT research, driven mainly by engineering and computer science disciplines. Five major research themes are identified, covering AI applications in agriculture, IoT-enabled smart farming, intelligent sensing systems, agricultural data management, and prediction models. Based on these findings, the study discusses the evolution of AIoT research, identifies current research gaps, and proposes a future research agenda focusing on artificial intelligence, interoperability, cybersecurity, data quality, technology adoption, sustainability, and responsible innovation. 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.
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
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
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.
Jobelle J. Capilitan, Abigael L. Balbin, Junrie B. Matias· AgriEngineering· 0 citations
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
Predictive intelligence in smart cities is one of the prevalent research domain for artificial intelligence and internet of things. However, there is no sufficient study on the mapping of its scholarly growth like thematic evolution and future trajectories. This study utilized bibliometric analysis on Scopus indexed documents from 2020 to 2025, applying PRISMA workflow, multi domain Boolean search strategy and analytical tools and science mapping techniques included in bibliometrix R-package and VOSviewer. Publication trends, citation structures, keyword co-occurrence mapping, co-authorship networks, thematic evolution and conceptual structure mapping were investigated. More or less 17,557 scopus-indexed publications were analyzed showing an annual growth rate of 16.42% where India, China and the United States lead global contributions. Predominant journals were found in the IEEE Internet of Things and IEEE Transactions on Intelligent Transportation Systems. Keyword co-occurrence revealed machine learning, deep learning, IoT, edge computing and smart city as the core thematic anchors and well-developed research areas. This study confirms that Artificial Intelligence and Internet of Things based forecasting in smart cities is a rapidly advancing research field. A roadmap for scholarly inquiry and policy-relevant applications can be explored where critical gaps and opportunities in areas such as federated learning, digital twins and cross-domain predictive analytics were highlighted.
Riz Rupert, L. Ortiz· Journal of Intelligent Decis...· 0 citations
Sustainable development has become a global priority in addressing environmental challenges while maintaining economic growth and social welfare. In the digital era, Artificial Intelligence (AI) and the Internet of Things (IoT) have emerged as key technologies that support the implementation of a green economy through efficient and data-driven resource management. This study aims to analyze the role of AI and IoT in supporting sustainable development, examine their contribution to improving resource efficiency, and identify the challenges associated with their implementation. The study employs a conceptual research method with a qualitative approach based on a literature review of recent scientific journals, books, and other relevant academic publications. The findings indicate that AI enhances decision-making, optimizes resource utilization, and reduces operational waste, while IoT enables real-time monitoring and automated management across various sectors. The integration of AI and IoT creates intelligent systems that improve energy efficiency, support environmental management, and strengthen green economy practices. However, challenges such as digital infrastructure gaps, limited human resources, high implementation costs, and data security concerns remain. Therefore, collaboration among governments, industries, and educational institutions is essential to maximize the contribution of AI and IoT to sustainable development.