Smart Agriculture: A Web-Based Framework for Effective Crop Recommendation Using Stack Ensemble Models

In the global context of rapidly developing countries, India places great importance on agriculture, which is crucial for supporting its population and making a substantial contribution to the national GDP. The selection of the right crop based on specific soil and weather conditions is essential for optimizing agricultural production. This paper proposes a Multi-Level Stack Ensemble Learning classification model integrated with a novel web framework by considering weather parameters and soil parameters data to offer personalized crop suggestions. It is designed with a user-friendly interface and caters specifically to farmers who may not have the expertise to input complex data. Extensive experiments have validated the system's effectiveness, demonstrating an impressive 93.01% accuracy and 91.83% Cohen's kappa score by Multi-Level Stack Ensemble Learning in crop recommendations, which is a significant improvement over traditional methods. This system combines cutting-edge technology with real-world agricultural requirements, boosting efficiency and equipping farmers with convenient, trustworthy, and tailored assistance. This results in promoting sustainable farming methods and a positive impact on the country's economic development.