Channel-attentive YOLOv5 and capsule auto-encoder for pomegranate disease detection

Fruits are the most vital items of global diets because of their rich nutritional value, thereby providing very high demand and agricultural revenues to the economy. Among the fruit crops, pomegranate is a valuable one due to its highest antioxidant potential. However, most crops of pomegranate suffer from diseases, which greatly reduce agricultural yield and productivity. Thus, along with the increasing demand of the fruit, early detection as well as classification of diseases will prove very crucial in boosting the yield and taking appropriate measures for prevention. We propose a segmentation-based model using deep learning in this paper to conduct disease identification in pomegranates The process begins with pre-processing images that is primarily an activity of cropping and resizing of the images, followed by enhanced Wiener filtering, which eliminates noise and enhances the clarity of the images The preprocessed images are then further segmented using a CA_YV5GC algorithm, (Channel Attentive YOLOv5-based Grab Cut), which isolates diseased regions from the images. Then the optimized ResNet-152 network is applied to acquire the fundamental features embedding the texture along with the shape characteristics which could identify ailments related symptoms. Coati Optimization is applied to choose the most dominant features in the lower dimensional representation of the extracted information for the classification of the disease. Ultimately, classification is performed using a Deep Capsule Canonical Auto-encoder (DC_CAENet) to classify the disease type with higher accuracy. Adaptive Osprey Optimization is used to optimize the parameters of the model. The existing methods are compared with that results proved this technique to be more accurate and efficient as compared to traditional techniques.