Disruptive technologies in agricultural operations: a systematic review of AI-driven AgriTech research

The evolving field of disruptive technologies has recently gained significant interest in various industries, including agriculture. The fourth industrial revolution has reshaped the context of agricultural technology (AgriTech) with applications of artificial intelligence (AI) and a strong focus on...

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Bibliographic Details
Published in:Annals of operations research 2022, Vol.308 (1-2), p.491-524
Main Authors: Spanaki, Konstantina, Sivarajah, Uthayasankar, Fakhimi, Masoud, Despoudi, Stella, Irani, Zahir
Format: Article
Language:English
Subjects:
Agriculture
Artificial intelligence
Business and Management
Combinatorics
Context
Data mining
Disruptive innovation
Evolution
Operations research
Operations Research/Decision Theory
S.I. : Artificial Intelligence in Operations Management
State-of-the-art reviews
Systematic review
Technology application
Theory of Computation
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Summary:The evolving field of disruptive technologies has recently gained significant interest in various industries, including agriculture. The fourth industrial revolution has reshaped the context of agricultural technology (AgriTech) with applications of artificial intelligence (AI) and a strong focus on data-driven analytical techniques. Motivated by the advances in AgriTech for agrarian operations, the study presents a state-of-the-art review of the research advances which are, evolving in a fast pace over the last decades (due to the disruptive potential of the technological context). Following a systematic literature approach, we develop a categorisation of the various types of AgriTech, as well as the associated AI-driven techniques which form the continuously shifting definition of AgriTech. The contribution primarily draws on the conceptualisation and awareness about AI-driven AgriTech context relevant to the agricultural operations for smart, efficient, and sustainable farming. The study provides a single normative reference for the definition, context and future directions of the field for further research towards the operational context of AgriTech. Our findings indicate that AgriTech research and the disruptive potential of AI in the agricultural sector are still in infancy in Operations Research. Through the systematic review, we also intend to inform a wide range of agricultural stakeholders (farmers, agripreneurs, scholars and practitioners) and to provide research agenda for a growing field with multiple potentialities for the future of the agricultural operations.
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-020-03922-z