A Machine Learning-Based Dynamic Model for Crop Suitability Using Rainfall and Soil Parameters

Author(s)	Sunil Yadav, Tanmay Dilip Jadhav, Om Suryakant Kakade, Prajakta Vishnu Pangare, Pooja Siddharam Bhutali
Country	India
Abstract	This project proposes a machine learning-based crop recommendation system that uses environmental factors like soil pH, nutrient levels (NPK), temperature, humidity, and rainfall to assist farmers in selecting the most suitable crop. Leveraging classification algorithms such as Logistic Regression and Decision Trees, the model provides accurate and localized crop suggestions to improve agricultural productivity. The system is designed to support precision farming and sustainability by minimizing input costs, reducing crop failure, and enhancing yield through data-driven decisions. It is scalable, adaptable to various regions, and can be integrated with IoT and mobile platforms for real-time, smart agriculture applications.
Keywords	Crop Recommendation, Decision Tree Classifier, Machine Learning, Soil pH, Rainfall, Agricultural Optimization
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-06-12
DOI	https://doi.org/10.71097/IJSAT.v16.i2.6174
Short DOI	https://doi.org/g9qqwg

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology