Crop Recomendation using Machine Learning.

Abstract

Agriculture remains one of the most essential industries, contributing significantly to food security and economic growth. However, many farmers face challenges in selecting the most suitable crops for their land, leading to inefficient farming and lower yields. This research explores how Artificial Intelligence (AI) and Machine Learning (ML) can help recommend the most appropriate crops based on soil characteristics, climate conditions, and historical data. Various ML algorithms, including Decision Trees, Random Forest, and Support Vector Machines (SVM), were applied to develop a model that provides accurate crop recommendations. The study found that Random Forest achieved the highest accuracy, showing the potential of AI-driven decision-making in agriculture.