Agro-Insight: Optimal Crop Recommendation for Soil Properties and Climatic Factors using Machine Learning Algorithms

DOI

10.2991/978-94-6239-768-2_3How to use a DOI?

Keywords

Agriculture; Crop Recommendation; SVM; Random Forest; Naive Bayes

Abstract

Agriculture is confronted with major challenges in choosing the most appropriate crops to be cultivated, as soil quality, climate fluctuation, and inefficient management of resources tend to result in low yields. To overcome this, we suggest a Machine Learning-based Crop Recommendation System that utilizes algorithms like SVM, Decision Trees, Random Forest, and Naive Bayes where Random-Forest gave us 99.32% accuracy. Our approach analyses key agricultural factors—soil nutrients, temperature, humidity, and rainfall—to provide evidence-based recommendations to farmers. Also, we have a Recommendation History Page, where past predictions and user comments are kept, enabling personalized insights and increasing the accuracy of the system in the long run. This enables better decision-making via the capability to monitor trends and alter decisions. Our system is especially useful for precision agriculture, resource management, and sustainable farming, ultimately empowering farmers with affordable, AI-based solutions to enhance productivity and efficiency. Future development will focus on enhancing usability and scalability to support mass deployment.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits any noncommercial use, sharing, 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 you modified the licensed material. You do not have permission under this license to share adapted material derived from this chapter or parts of it.

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TY  - CONF
AU  - Siyona Snotra
AU  - Vaibhav Tomar
AU  - Neha Singhal
PY  - 2026
DA  - 2026/09/07
TI  - Agro-Insight: Optimal Crop Recommendation for Soil Properties and Climatic Factors using Machine Learning Algorithms
BT  - Proceedings of the Third International Conference on Recent Advances in Computing Sciences (RACS 2025)
PB  - Atlantis Press
SP  - 19
EP  - 27
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-768-2_3
DO  - 10.2991/978-94-6239-768-2_3
ID  - Snotra2026
ER  -