Crop Prediction using Machine Learning

Abstract

Crop prediction plays a crucial role in modern agriculture by helping farmers make informed decisions about what crops to plant, ensuring optimal yields, and reducing resource wastage. This study explores the application of machine learning algorithms, specifically Random Forest, Decision Tree, and Passive Aggressive algorithms, for predicting the best suited crop based on various environmental and soil parameters. The input features considered for prediction include temperature, humidity, pH, rainfall, and soil nutrients (Nitrogen, Phosphorus, Potassium), while the output is the recommended crop name. A dataset consisting of these parameters was used to train and evaluate the models. The performance of each algorithm was compared based on their accuracy in correctly predicting the appropriate crop. Results indicate that machine learning models, especially Random Forest, show promising results in crop prediction by effectively utilizing environmental and soil data to provide accurate recommendations. This approach offers a scalable solution for precision agriculture, helping farmers optimize crop selection, improve productivity, and manage resources more efficiently.