Plant Disease Detection and Classification Using Bacteria Foraging Optimization Algorithm Through Convolution Neural Network

Abstract

Humans can recognize the plants infected by diseases but separated from our visual perception it is hard to recognize plant diseases. In croplands without taking the right care and prompt action, the entire field may become a region afflicted by diseases. So we identify the plant diseases ahead of time with the assistance of present-day computer technologies. An advanced model was introduced to accurately recognize and classification plant diseases. Here we proposed an approach that can use the Convolutional Neural Network (CNN) based on BFOA for distinguishing diseases in plants. The input picture for the extraction of features is divided into 3 clusters by the Euclidean distance measurement metric of the k-mean algorithm and from the ROI, parameters of the GLCM matrix are calculated in the same cluster prior to BFOA. Assigning matrix parameters as BFOA input improves the network’s accuracy and efficiency in determining. In classification, we proposed a Convolutional Neural Network (CNN) using ResNet50 as a pre-trained network in deep learning toolbox which classifies from a given dataset. The approach is more reliable as the detection and classification of plant diseases are more precise.