Taylor Remora optimization enabled deep learning algorithms for percentage of pesticide detection in grapes.

In the world, grapes are considered as the most significant fruit, and it comprises various nutrients, like Vitamin C and it is utilized to produce wines and raisins. The major six general grape leaf diseases and pests are brown spots, leaf blight, downy mildew, anthracnose, and black rot. However, the existing manual detection methods are time-consuming and require more efforts. In this paper, an effectual grape leaf disease finding and percentage of pesticide detection approach is devised usingan optimized deep learning scheme. Here, the input image is pre-processed and then, black spot segmentation is done using proposed Taylor Remora Optimization Procedure (TROA). The TROA is the combination of Taylor concept and Remora Optimization Algorithm (ROA). After that, the multi-classification of grape leaf disease is performed to classify the disease as Black rot, Black measles, Isariopsis leaf spot and healthy. Accordingly, the training process of the Deep Neuro-Fuzzy Optimizer (DNFN) is done using Sine Cosine Butterfly Optimization (SCBO). Then, pesticide classification is done using Deep Maxout Network (DMN) and the training of the DMN is done using the Monarch Anti Corona Optimization (MACO) algorithm. Finally, the pesticide percentage level detection is performed using Deep Belief Network (DBN), which is trained by the TROA. The devised scheme obtained highest accuracy of 0.9327, sensitivity of 0.9383, and 0.9429. Thus, this method can assist as an effectual decision provision system for assisting the farmers to find the percentage of pesticide affected in grape leaf diseases.