CROP RECOMMENDATION AND YIELD PREDICTION SYSTEM USING GROUP OF MACHINE LEARNING TECHNIQUES

Abstract

This study presents an enhanced crop yield prediction and recommendation system that integrates advanced machine learning techniques with a user-centric decision support framework for precision agriculture. Building upon ensemble-based approaches, the proposed mechanism incorporates multiple predictive models, including Random Forest, Gradient Boosting, Support Vector Machines, and stacking strategies, to capture complex relationships between soil properties, climatic variables, and crop performance. The system utilizes key agricultural features such as nitrogen, phosphorus, potassium levels, pH, temperature, rainfall, humidity, and cultivated area to generate accurate yield forecasts and recommend optimal crops. A robust preprocessing pipeline involving feature scaling and categorical encoding ensures improved model generalization and stability. Unlike conventional researchfocused models, this work emphasizes practical deployment through an interactive interface that enables farmers to input field-specific parameters and receive real-time, data-driven insights. The proposed framework not only improves prediction accuracy but also supports efficient resource utilization, risk reduction, and sustainable farming practices. Additionally, the system is designed with scalability in mind, allowing future integration with IoT devices, remote sensing technologies, and automated monitoring systems. Experimental evaluation demonstrates that ensemble-based models achieve superior performance compared to individual algorithms, validating the effectiveness of the proposed approach in real-world agricultural scenarios.