Deep Learning-Powered Crop Disease Diagnosis and Fertilizer Guidance System

Predicting leaf diseases is vital for precision agriculture, enabling early detection, reducing losses, and improving yields. Traditional models often lacked adaptability across multiple crops and failed to provide actionable recommendations, limiting their practical use. This project overcomes these challenges by integrating disease detection and tailored treatment advice into a unified system for crops like paddy, tomato, potato, blackgram, and mirchi. Utilizing advanced deep learning models such as VGG16, ResNet50, and Deep CNN, the system delivers robust multi-class disease classification. VGG16 excelled with 97% accuracy in detecting and classifying diseases. The inclusion of a recommendation system adds significant value, offering effective disease management strategies and empowering farmers to make informed decisions, enhancing productivity and sustainability in agriculture.