Rainfall Crop Advisory (Location-Based) System Using Machine Learning

DOI

10.2991/978-94-6239-713-2_26How to use a DOI?

Keywords

Crop Recommendation; Machine Learning; Yield Estimation; Climate Analytics; Soil Nutrients; Rainfall Prediction

Abstract

Because soil, climate, and local environment affect growing conditions to varying degrees, it has always been difficult to determine what crops to plant and how to predict a harvest’s yield. Traditional crop-selection and yield-prediction tools have relied on fixed data and have therefore been limited in their applicability at the field level. To address the reliance of traditional agriculture on static tools and processes, a location-based crop advisory system is developed using machine learning models such as Random Forest, Gradient Boosting, and Support Vector Machine (SVM). Machine learning models such as Random Forest, Gradient Boosting, and Support Vector Machines are used for prediction and have been matched to archived farming records as well as local climatological patterns for each specific location. Experimental results show that Random Forest and Gradient Boosting achieved over 98% accuracy. These models use environmental and geographical data to improve prediction accuracy, and both Random Forests and Gradient Boosting achieved over 98% accuracy in yield prediction testing.

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  - D. Nirosha
AU  - M. Surya Bhupal Rao
AU  - Kalugotla Sairam
AU  - Desireddy Sai Ram
AU  - Mulinti Kalam
AU  - Saginala Rahul Vishal
PY  - 2026
DA  - 2026/06/25
TI  - Rainfall Crop Advisory (Location-Based) System Using Machine Learning
BT  - Proceedings of the International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA 2026)
PB  - Atlantis Press
SP  - 351
EP  - 365
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-713-2_26
DO  - 10.2991/978-94-6239-713-2_26
ID  - Nirosha2026
ER  -