A novel probabilistic intermittent neural network (PINN) and artificial jelly fish optimization (AJFO)-based plant leaf disease detection system

Plant leaf disease identification and classification are the most essential and demanding tasks in the agriculture field. In traditional researches, various automated detection technologies have been developed with the goal of more accurately identifying plant leaf disease. Nevertheless, it faces some problems related to complex mathematical modeling, increased time consumption, processing overhead, and mis-prediction results. Therefore, a novel probabilistic intermittent neural network and artificial jelly fish optimization-based plant leaf disease detection system is proposed in this paper. The proposed work aims to "make a new detection scheme to identify correctly plant leaf disease from the given dataset." Here, the probabilistic intermittent neural network (PINN) classification technique is used to predict label as normal or affected by disease. If it is disease affected, the residual multi-scale Unet segmentation (RMUNet) segmentation technique is applied to segment the disease affected region. Finally, the simulation outcomes confirm the efficiency of the proposed leaf disease identification system under some variables.