Crop Recommendation System Using Machine Learning

Abstract

Agriculture continues to be the backbone of India’s economy, supporting livelihoods and contributing significantly to food security. However, farmers frequently face challenges in deciding which crop to cultivate due to unpredictable weather conditions, variations in soil fertility, and limited access to scientific tools. Traditional methods of crop selection are primarily experience-based and often lead to suboptimal productivity. To address these challenges, this paper presents a Machine Learning (ML)-based Crop Recommendation System designed to recommend the most appropriate crop for cultivation based on soil nutrients and environmental parameters. The system utilizes attributes such as Nitrogen (N), Phosphorus (P), Potassium (K), pH, temperature, humidity, and rainfall. These features are analyzed using multiple ML algorithms, including Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest. After extensive testing, the Random Forest Classifier demonstrated the highest performance, achieving an accuracy of 95%, due to its ensemble-based approach and ability to handle nonlinear feature interactions. The proposed system enables farmers to make informed, data-driven decisions rather than relying solely on experience. The study highlights how integrating ML with precision agriculture can lead to sustainable farming practices, efficient resource utilization, and improved crop yield.