Machine Learning Algorithms for Crop Recommendation in Precision Agriculture

This research paper presents a comparative study of various machine learning algorithms applied to crop recommendation systems in the context of precision agriculture.With the increasing demand for food production and the need for sustainable farming practices, the integration of machine learning techniques has become essential for optimizing crop yield and resource management.The study evaluates several algorithms, including Decision Trees, Random Forests, Support Vector Machines, and Neural Networks, assessing their performance based on accuracy, precision, recall, and computational efficiency.A comprehensive dataset comprising soil characteristics, climate conditions, and crop history is utilized to train and validate the models.The results indicate significant differences in the effectiveness of each algorithm, providing insights into their strengths and limitations for crop recommendation.The findings aim to assist farmers, agronomists, and policymakers in making informed decisions to enhance agricultural productivity while minimizing environmental impact.This research contributes to the growing body of literature on the application of artificial intelligence in agriculture and offers a framework for future developments in crop management systems.