Soil Based Crop Recommendation System Using Machine Learning

Abstract: Efficient crop selection plays a crucial role in enhancing agricultural productivity and sustainability. Traditional farming practices often rely on farmers’ experience and general guidelines, which may not consider local soil characteristics and environmental variations. This study proposes a soil-based crop recommendation system using machine learning techniques to support data-driven agricultural decisions. The system utilizes soil parameters such as pH, nitrogen (N), phosphorus (P), potassium (K), temperature, humidity, and rainfall to predict the most suitable crop for a given region. A dataset comprising soil and environmental attributes was preprocessed and analyzed to train various classification models, including Decision Tree, Random Forest, Support Vector Machine (SVM), and Gradient Boosting algorithms. Performance evaluation based on accuracy, precision, recall, and F1-score demonstrates that ensemble learning methods outperform traditional classifiers. The proposed model provides a reliable, scalable, and user-friendly solution for optimizing crop selection, improving yield, and promoting sustainable agricultural practices. Future work includes integrating real-time IoT sensor data and satellite imagery for dynamic recommendations.

Keywords: Crop recommendation, machine learning, soil analysis, precision agriculture, Random Forest, data-driven farming, sustainable agriculture, decision support system. .