Researchers propose an integrated framework combining multi-spectral imaging, deep learning, and federated learning for precision crop disease diagnosis in low-resource farming communities. The framework addresses persistent challenges where crop diseases cause significant losses and threaten food security, while conventional visual inspection or simple image-based methods lack precision, scalability, and real-time capability. Existing systems also consume tremendous computational resources, rely on centralized data storage, and lack data privacy protection. Key innovations include the 3D Spectral-Spatial Convolutional Neural Network (3D-SSCNN) for hyperspectral feature extraction, the Federated Disease Diagnosis Network (Fed-DiagNet) for decentralized model training, and the Temporal Progression LSTM (TP-LSTM) for modeling disease progression over time. A Multimodal Transfer Adaptive Network (MTAN) fuses hyperspectral and environmental data, while a Reinforcement Learning-Based Feedback Optimization (RL-FO) provides treatment recommendations based on specific conditions. The framework aims to raise diagnostic accuracy, improve real-time efficiency, and enhance scalability, enabling low-resource farming communities to access adaptive technology for countering crop diseases and promoting food security and responsible agriculture.