Development of a Machine Learning-Based System for Optimizing Crop Recommendations

In precision agriculture, crop recommendation systems play a crucial role in enhancing crop productivity. This research paper proposes a machine learning-based crop recom mendation system that leverages climatic variables—such as tem
perature, humidity, and rainfall—as well as soil characteristics,including nitrogen, potassium, and pH levels. Utilizing a dataset that integrates soil parameters, climate data, and corresponding crop yield information, we aim to train and evaluate several machine learning algorithms to determine their efficacy in providing
crop recommendations. By comparing the performance of these
algorithms, our proposed system is designed to assist farmers and agricultural experts in selecting and managing crops more effectively, thereby improving overall crop yields and productivity