Crop Recommendation and Yield Prediction Based on Geographical Location with Web Interface

Agriculture is crucial in maintaining the global economy and food security. Yet, farmers sometimes have difficulty choosing the most appropriate crops and estimating yield prospects because of the varying climatic conditions, soil types, and availability of resources. This project seeks to overcome this difficulty through designing an internet-based intelligent crop recommendation and yield prediction system using geographical location information. The system involves integrating machine learning algorithms with geospatial information to study environmental factors like soil, temperature, rain, and humidity. By using past agricultural data and current weather inputs, the model prescribes best crops based on geographical locations and forecasted yields. The system is implemented via a simple web-based interface, through which farmers and stakeholders can feed location-specific inputs and receive insights immediately. This methodology not only improves farmers' decision-making but also helps in precision agriculture, optimization of resources, and sustainable farming. The modular design provides scalability and flexibility to accommodate various regions and crops, thus making the system a valuable instrument in contemporary agritech solutions.