A Systematic Review of Artificial Intelligence Applications Across the Agricultural Value Chain: Opportunities, Limitations and Ethical Frontiers

DOI

10.2991/978-94-6239-791-0_11How to use a DOI?

Keywords

Artificial intelligence; precision agriculture; agricultural value chain; machine learning; ethical AI; smart farming; systematic review

Abstract

Climate change, declining resources, population growth and increased food demand are increasingly threatening the profitability of farming and demand greater efficiency and sustainability in production systems. Artificial Intelligence, or AI, is a technology that can affect how decisions are made, how resources are managed, and how much food can be grown in agriculture. This study is a systematic review of the various applications of AI throughout the agriculture value chain, from crop production, disease and pest detection through precision agriculture, post-harvest management, supply chain optimization, sustainability and emerging generative AI applications. Citing the PRISMA 2020 model, all literature published from 2016 to 2025 were gathered from different scientific databases and filtered according to the inclusion and exclusion criteria. There were a total of 214 records identified, 80 of which were potential full-text articles, and finally 34 articles were included in the qualitative synthesis. The results reveal that using AI technologies such as machine learning, deep learning, computer vision, robotics, and systems powered by the Internet of Things (IoT) can greatly help in the prediction of yields, disease diagnosis, smart irrigation, automation, resource optimization, and food traceability. In addition, key barriers to adoption highlighted in the review are data quality challenges, lack of digital infrastructure, hefty implementation costs, scalability problem, and issues around data ownership, transparency and fairness. Additionally, the governance and ethical challenges of the responsible and proper use of AI are explored, especially within smallholder farming communities. The study concludes that AI shows great promises concerning its contribution to agricultural productivity, sustainability, and resilience, but that the long-term effects of its use rely on the proper integration of inclusive policy, the need for explanation of the systems and their use in the field, and the adoption of governance frameworks that are suitable for actual application. The results give a complete overview of the current state of the art and future research directions for improving responsible AI integration in agriculture systems.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Abdul Hannan R. Dalal
AU  - Mohammed Danish Kolhar
AU  - Yuvraj Lahoti
PY  - 2026
DA  - 2026/10/09
TI  - A Systematic Review of Artificial Intelligence Applications Across the Agricultural Value Chain: Opportunities, Limitations and Ethical Frontiers
BT  - Proceedings of the International Conference on Artificial Intelligence and Machine Learning for Agro-Ecosystems (ICAIML-AE 2026)
PB  - Atlantis Press
SP  - 107
EP  - 128
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-791-0_11
DO  - 10.2991/978-94-6239-791-0_11
ID  - Dalal2026
ER  -