Enhancing Crop Productivity: Integrated Solutions for Crop Yield, Crop Recommendation, and Crop Disease Management

The paper presents an integrated approach for crop disease prediction, crop recommendation, and crop yield prediction to address the challenges in modern agriculture. The research leverages diverse agricultural datasets, including historical crop disease records, soil composition data, climate variables, and past crop yield records. Machine learning algorithms, such as decision trees, support vector machines, and neural networks, are utilized to develop predictive models for each component. The integration of these models creates a unified platform that facilitates seamless information flow and decision-making for farmers and agronomists. The system's performance is assessed using various evaluation metrics, ensuring the accuracy and effectiveness of disease detection, personalized crop recommendations, and crop yield forecasts. The experimental outcomes contribute to advancing sustainable farming practices and enhancing agricultural productivity. The proposed integrated system holds great promise in revolutionizing modern agriculture and ensuring food security in a changing world.