Development of a Machine Learning-Based System for Optimizing Crop Recommendations
Vinay Tomar, Gaurav Sharma, R. Rajkumar, Akanksha Singh, Sarvendra Singh Dhakare, Kamlesh Kumar
Abstract
In precision agriculture, crop recommendation systems play a crucial role in enhancing crop productivity. This research paper proposes a machine learning-based crop recommendation system that leverages climatic variables—such as temperature, 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