Multi-Task Distillation Learning for Coffee Corticium Salmonicolor Pink Berry Disease for Real-Time Prediction

Coffee Corticium Salmonicolor Pink Berry Disease (CCSPBD) is a substantial risk to coffee cultivation. Precise and prompt identification is essential for efficient disease treatment. This research introduces a multi-task distillation learning (MTDL) method for the real-time prediction of CCSPBD. A more extensive teacher model is trained on a comprehensive dataset of coffee plant diseases, and its knowledge is conveyed to a smaller student model tailored for CCSPBD prediction. Integrating supplementary activities about coffee plant health into the teacher model enhances the student model's comprehension of plant disease patterns. The resultant student model can precisely forecast CCSPBD in real-time, allowing farmers to implement prompt interventions to avert crop loss. Experimental findings indicate the efficacy of the suggested MTDL method in attaining high accuracy and efficiency for CCSPBD prediction.