Artificial Intelligence in Precision Agriculture: A Systematic Survey of Technologies, Applications, and Future Research Directions

DOI

10.2991/978-94-6239-791-0_18How to use a DOI?

Keywords

Artificial Intelligence; Precision Agriculture; Deep Learning; Smart Farming; Internet of Things; Sustainable Agriculture; Systematic Literature Review

Abstract

This study uses the Technology Acceptance Model (TAM) and Diffusion of Innovation to analyze the rapidly growing literature on precision agriculture (PA) AI applications. Data-driven agricultural decision-making using artificial intelligence is increasingly vital as agricultural systems are expected to enhance productivity without compromising environmental sustainability or resource efficiency. PRISMA 2020 is used to evaluate 150 peer-reviewed publications from Scopus, Web of Science, and IEEE Xplore from 2015 to 2025. To detect research trends and clusters, we used CiteSpace-based bibliometric analysis on chosen papers. We classified AI and agriculture using Braun and Clarke’s theme analysis. AI-driven precision agricultural research is 55% deep learning, 8% machine learning, 11% IoT systems, and 6% robots and automation. Deep learning models forecast 92% accuracy in image-based applications like plant disease diagnosis and crop monitoring, compared to 85% for traditional machine learning. Most common applications include crop monitoring (35%), yield prediction (5%), and pest and disease detection (18%). Data interoperability, infrastructure, scale, and acceptance remain issues, especially in developing agriculture. Combining smart farming ecosystem models with DOI theory, expanding TAM to include infrastructure availability as a critical factor in AI adoption, and presenting an AI-precision agriculture framework that links technical capacities with agricultural decision-making procedures expands knowledge. These results may help academics, policymakers, and agricultural stakeholders digitalize sustainable agriculture.

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.

Download article (PDF)

TY  - CONF
AU  - Tarun Madan Kanade
AU  - Shuchi Gautam
AU  - Jonathan Sudhir Joseph
AU  - Tushar Savale
AU  - Radhakrishna Batule
PY  - 2026
DA  - 2026/10/09
TI  - Artificial Intelligence in Precision Agriculture: A Systematic Survey of Technologies, Applications, and Future Research Directions
BT  - Proceedings of the International Conference on Artificial Intelligence and Machine Learning for Agro-Ecosystems (ICAIML-AE 2026)
PB  - Atlantis Press
SP  - 208
EP  - 238
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-791-0_18
DO  - 10.2991/978-94-6239-791-0_18
ID  - Kanade2026
ER  -