Smart Agriculture: Deep Learning-Based Crop Disease Classification, Prediction, and Treatment Recommendation

Crop-related diseases represent a major challenge to agricultural output and threaten global food availability, leading to substantial economic losses. Traditional disease detection methods, that depend on manual examination are typically time-consuming and susceptible to errors. This paper presents a novel approach to crop disease prediction through an enhanced deep learning framework based on the ResNet-50 architecture. By integrating advanced data augmentation techniques, memory optimization via checkpointing, and mixed precision training, the proposed model achieves an impressive accuracy of 96.65% in disease classification. The model is deployed within a web application that provides real-time diagnostic support and preliminary treatment recommendations to farmers, enabling timely interventions. This research offers a scalable solution to improve crop management, reduce losses, and promote more efficient agricultural practices.