Artificial intelligence research in agriculture: a review

Purpose The purpose of this paper is to provide a comprehensive review on the academic journey of artificial intelligence (AI) in agriculture and to highlight the challenges and opportunities in adopting AI-based advancement in agricultural systems and processes. Design/methodology/approach The authors conducted a bibliometric analysis of the extant literature on AI in agriculture to understand the status of development in this domain. Further, the authors proposed a framework based on two popular theories, namely, diffusion of innovation (DOI) and the unified theory of acceptance and use of technology (UTAUT), to identify the factors influencing the adoption of AI in agriculture. Findings Four factors were identified, i.e. institutional factors, market factors, technology factors and stakeholder perception, which influence adopting AI in agriculture. Further, the authors indicated challenges under environmental, operational, technological, economical and social categories with opportunities in this area of research and business. Research limitations/implications The proposed conceptual model needs empirical validation across countries or states to understand the effectiveness and relevance. Practical implications Practitioners and researchers can use these inputs to develop technology and business solutions with specific design elements to gain benefit of this technology at larger scale for increasing agriculture production. Social implications This paper brings new developed methods and practices in agriculture for betterment of society. Originality/value This paper provides a comprehensive review of extant literature and presents a theoretical framework for researchers to further examine the interaction of independent variables responsible for adoption of AI in agriculture. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2020-0448