A Study of the Application of Machine Learning to the Crop Recommendation System

Author(s)	Ms. Ekta Patel, Prof. Dr. Hiren Jayantilal Dand, Dr. Neelam Labhade
Country	India
Abstract	Over 60% of India's workforce is employed in the agricultural sector, which contributes about 17% of the nation's GDP. With the advent of new technology like vertical farming and others, this area has witnessed some changes. Nonetheless, a lot of Indian farmers continue to use their property in accordance with customs and beliefs. For instance, instead of adapting to weather fluctuations, they wait for the weather to suit their farming methods. By forecasting which crops are compatible with the variables that affect crop growth, such as soil nutrients, soil pH, humidity, and rainfall, our research aims to assist farmers in selecting the optimum crops for their circumstances and surroundings. We employ a variety of machine learning models, including Gaussian Naïve Bayes (GNB), Logistic Regression (LR), Decision Tree (DT), and Support Vector Machine (SVM).
Keywords	Machine Learning (ML), Crop suggestion, Nitrogen-Phosphorus- Potassium (NPK), Humidity, Rainfall, pH, Decision Tree (DT), Logistic Regression (LR), and Gaussian Naïve Bayes (GNB).
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 1, January-February 2026
Published On	2026-01-10
DOI	https://doi.org/10.63363/aijfr.2026.v07i01.2864
Short DOI	https://doi.org/hbk6wj

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