AI Based Crop Recommendation for Farmers

Abstract

Agriculture plays a significant role in the economic development and food security of many countries. Selecting the appropriate crop based on soil nutrients and climatic conditions is essential for achieving higher agricultural productivity and sustainable farming. However, traditional crop selection methods often rely on farmers' experience and manual analysis, which may result in poor crop yield and inefficient utilization of resources. To overcome these limitations, this paper presents an AI Based Crop Recommendation System for Farmers that utilizes machine learning techniques to recommend the most suitable crop for cultivation based on soil and environmental parameters. Three algorithms, namely Random Forest, Logistic Regression, and Support Vector Machine (SVM), are trained and compared to identify the model with the highest prediction accuracy. Farmers can securely register, log in, enter agricultural parameters, and receive instant crop recommendations along with cultivation suggestions. The system also maintains prediction history, enabling users to review previous results. By integrating artificial intelligence with agriculture, the proposed solution improves crop selection, enhances productivity, optimizes resource utilization, and supports sustainable farming practices.