AI-Based Crop Recommendation for Farmers

Abstract

Agriculture is one of the most important sectors that supports the economy and provides food for the growing population. However, many farmers face difficulties in selecting the right crop because soil conditions and weather factors vary from place to place. Choosing an unsuitable crop can reduce productivity and increase financial loss. This project presents an AIBased Crop Recommendation System for Farmers that uses Artificial Intelligence (AI) and Machine Learning (ML) to recommend the most suitable crop based on soil and environmental conditions. The system analyzes important factors such as nitrogen (N), phosphorus (P), potassium (K), temperature, humidity, soil pH, and rainfall to predict the best crop. Different machine learning algorithms, including Random Forest, Decision Tree, and KNearest Neighbors (KNN), are trained and compared to select the most accurate model. The selected model is integrated into a simple web application developed using Python and Flask, allowing users to enter input values and receive instant crop recommendations. The proposed system helps farmers make informed decisions, improve agricultural productivity, reduce the risk of incorrect crop selection, and encourage sustainable farming through accurate, reliable, and data-driven recommendations.