Crop Prediction Analysis And Plant Disease Detection Using Machine Learning

The development of a comprehensive agricultural support system, integrating plant disease classification, crop prediction, and fertilizer recommendation models. Leveraging the ResNet-9 architecture, the plant disease classification model accurately identifies diseases in crop leaves. Additionally, the system incorporates crop prediction capabilities to forecast suitable crops based on environmental conditions and historical data. Furthermore, the fertilizer recommendation model suggests optimal fertilizers based on soil composition, crop type, and nutrient requirements. This multifaceted approach facilitates precision agriculture by enabling farmers to make informed decisions regarding crop health management, crop selection, and fertilization practices. The integration of these models provides a holistic solution for enhancing agricultural productivity, optimizing resource utilization, and promoting sustainable farming practices. Future enhancements may involve refining the prediction algorithms, incorporating real-time sensor data for dynamic recommendations, and scaling the system for broader adoption in diverse agricultural settings. Overall, the project demonstrates the potential of AI-driven solutions to address complex challenges in agriculture and contribute to global food security efforts.