Transfer Learning Approaches for Leaf Diseased Portion Detection

Overview – In this study, The difficulties in iden- tifying and classifying diseased areas on leaves prompted us to create a deep block attention solid-state drive (SSD) for identifying diseased areas on leaves and classifying their severity. We propose a novel leaf disease portion method. Squeeze- and-excitation SSD (Se-SSD), and DBA-SSD approaches are recommended for finding plant leaves.SSD feature extraction network and channel attention mechanism are coupled by Se SSD, VGG feature extraction network is enhanced by DB SSD, and channel attention mechanism is coupled by DBA SSD. Convolutional layers trained on Image Net images using the VGG model are transferred to this model, reducing training time and speeding up the training process. On the other hand, the collected images containing plant leaves are randomly split into a training set and a test setup .1:1 ratio. After careful consideration, we chose the Leaf Village dataset because it contains imagery relevant to the study area. This collection contains leaf images, including images of cotton leaves. Image data is enhanced using data enhancement techniques such as horizontal flipping and histogram equalization. In this study, we examine and contrast the enhanced performance of VGG16 and RCNN with that of the more conventional Faster RCNN and Single Shot target identification approaches operating in the same setting. Comparing its performance with that of other target identification algorithms, we find that SSD and faster RCNN outperforms other algorithms in terms of algorithm.