Agricultural Crop Recommendations Based on Productivity and Season

Abstract

This chapter aims to develop an agricultural crop recommendation system leveraging the power of machine learning algorithms. The proposed system takes into account crop productivity and prevailing season as crucial factors in making appropriate crop suggestions. The authors proposed the SVM algorithm, which was trained and evaluated on a comprehensive dataset comprising historical agricultural data with diverse features such as climate variables, soil properties, and geographical factors. The data was further segmented based on seasonal patterns to provide crop recommendations tailored to specific timeframes. The models' performance was evaluated using standard metrics, and an ensemble approach was considered to enhance the system's robustness. Ultimately, the developed system offers farmers and agricultural experts a valuable tool for making informed decisions, optimizing crop selection, and increasing overall agricultural productivity