A Review on AI-Driven Smart Crop Advisory Systems for Small and Marginal Farmers

DOI

10.2991/978-94-6239-713-2_29How to use a DOI?

Keywords

Crop Recommendation; Disease Detection; CNN; RandomForestClassifier; XGBoost; Weather-based Advisory; Smart Agriculture; Small and Marginal Farmers

Abstract

The Indian economy relies heavily on agriculture, yet crop diseases, poor disease diagnosis, and inaccurate crop selection continue to cause significant reductions in crop yield and farmers’ income. Early and accurate disease detection, data-driven crop recommendation, and weather-based advisory can help prevent these losses and promote sustainable farming practices. Recent advances in artificial intelligence and machine learning have enabled the development of automated advisory systems using techniques such as ensemble models and convolutional neural networks (CNNs). This review focuses on the key components of an integrated smart crop advisory system, highlighting the performance and effectiveness of machine learning and deep learning models in this domain. It presents a literature review of (1) crop recommendation approaches using models like Random Forest and XGBoost, (2) plant disease detection using various CNN architectures, and (3) weather-based advisory integration. The paper also evaluates the strengths, limitations, and real-time applicability of these solutions. This review compares and contrasts the available machine learning methods applied in smart agricultural advisory systems to detect plant diseases and make crop recommendations.

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  - Kotagiri Kulbhushan
AU  - Priyanka Gupta
AU  - Gorja Poornima
AU  - Nallabolu Poojitha
AU  - Vivek Maddula
AU  - Uppala Sahith
PY  - 2026
DA  - 2026/06/25
TI  - A Review on AI-Driven Smart Crop Advisory Systems for Small and Marginal Farmers
BT  - Proceedings of the International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA 2026)
PB  - Atlantis Press
SP  - 383
EP  - 393
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-713-2_29
DO  - 10.2991/978-94-6239-713-2_29
ID  - Kulbhushan2026
ER  -