CROP CLASSIFICATION AND YIELD PREDICTION USING ROBUST MACHINE LEARNING MODELS USING AGRICULTURAL SUSTAINABILTY

Abstract

Agriculture plays a crucial role in ensuring food security and supporting the economic development of many countries. However, variations in climatic conditions, soil characteristics, rainfall, temperature, and other environmental factors make crop selection and yield prediction challenging for farmers. Accurate prediction of crop yield and appropriate crop recommendation can significantly improve agricultural productivity while reducing financial risks. This project presents an intelligent machine learning-based system for crop classification and yield prediction using historical agricultural data. The proposed system analyzes multiple parameters such as soil type, rainfall, temperature, humidity, cultivation area, and seasonal conditions to recommend suitable crops and estimate their expected yield. Various machine learning algorithms, including Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, Logistic Regression, and Gradient Boosting, are evaluated to identify the most accurate prediction model. Among these, the Random Forest algorithm demonstrates superior performance in terms of prediction accuracy and reliability. The system is implemented using Python, Django, and MySQL to provide a user-friendly web application where users can register, input agricultural parameters, and obtain real-time crop recommendations and yield predictions. By enabling data-driven decision-making, the proposed system helps farmers optimize resource utilization, improve crop productivity, and promote sustainable agricultural practices. Furthermore, the system can be extended by integrating realtime IoT sensor data and weather forecasting services to enhance prediction accuracy and support smart farming applications.