RMODCNN: A Novel Plant Disease Prediction Framework

The detection of plant diseases is essential for avoiding reductions in agricultural product production and quantity. Visually observable patterns on the plant are the main focus of plant disease research. Monitoring plant health and spotting diseases is crucial for sustainable agriculture. Monitoring plant diseases manually is very difficult. It necessitates a lot of labor, expertise in plant diseases and prolonged processing. In the presented research, a ratal mellifera based DCNN model is developed for plant disease prediction. At first, the data is acquired out of a plant leaf input dataset, further, it involves pre-processing for removing noise. In the next step, the Region-of-Interest (ROI) extraction strategy is utilized for separating the relevant regions to process further process out of the unessential pixels. Further extracted ROI outcome will be imposed to the process of data augmentation; further GAN-based approach has been utilized for data augmentation for data enhancement for enhancing the accuracy of prediction. Further feature extraction has been accompanied for extracting features involving RESNET 101, LTP and LOOP as well as statistical features. Ratal mellifera optimization is derived out of two optimization strategies namely ratal optimization along with mellifera bee optimization, that is utilized for optimizing the channel boosted deep CNN network. Taking into consideration the TP and k-fold, the performance key indicators accuracy (acc) and sensitivity (sen) for TP for tea leaf prediction and for apple leaf prediction have demonstrated better outcomes in contrast to recent state-of-the-Art has been gained.