AI-Driven Smart Agriculture: A Bibliometric Review of Research Progress and Local Commodity Potential

DOI

10.2991/978-94-6463-926-1_69How to use a DOI?

Keywords

Artificial Intelligence; Bibliometric Analysis; Local Commodity Development; Precision Farming; Smart Agriculture

Abstract

The global agricultural sector faces significant challenges from climate change and increasing food demand, with Artificial Intelligence (AI) emerging as a transformative solution for optimizing resources and enhancing productivity. This study presents a comprehensive bibliometric analysis of AI-driven smart agriculture research through systematic dual-database integration of Scopus and Web of Science, analyzing 1,248 unique articles published from 2018–2024. Using rigorous deduplication procedures and advanced co-occurrence analysis, 530 keywords meeting minimum occurrence thresholds were identified and clustered using VOS viewer. The analysis revealed eight distinct research clusters: Precision Agriculture & Remote Sensing (89 keywords), AI Methods & Agricultural Applications (76 keywords), Computer Vision & Image Processing (94 keywords), Agricultural Robotics & Automation (68 keywords), Environmental Monitoring & IoT Integration (73 keywords), Crop Management & Protection (82 keywords), Data Processing & Analysis (33 keywords), and Specialized Applications (15 keywords). Geographic collaboration analysis identified 53 countries with substantial research contributions, while citation impact assessment of the top 20 most-cited papers (citation range: 297–1,332) revealed that papers containing keywords relevant to tropical and subtropical systems comprised 60% of highly-cited research. Applied to regional agricultural contexts, the cluster analysis indicates that computer vision technologies, environmental monitoring systems, and crop management applications show documented performance metrics suitable for local commodities including rice, horticulture, and plantations. However, implementation considerations include technical barriers such as infrastructure requirements, economic accessibility factors, and training program needs. This bibliometric mapping provides a systematic framework for understanding AI agricultural research development and offers evidence-based guidance for technology adaptation in regional agricultural contexts.

Copyright

© 2025 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  - Haryati Haryati
AU  - Dwi Vernanda
AU  - Nunu Nugraha Purnawan
PY  - 2025
DA  - 2025/12/31
TI  - AI-Driven Smart Agriculture: A Bibliometric Review of Research Progress and Local Commodity Potential
BT  - Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2025 (iCAST-ES 2025)
PB  - Atlantis Press
SP  - 617
EP  - 624
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-926-1_69
DO  - 10.2991/978-94-6463-926-1_69
ID  - Haryati2025
ER  -