Channel–Spatial Segmentation Network for Classifying Leaf Diseases

Abstract

Agriculture is an important resource for the global economy, while plant disease causes devastating yield loss. To control plant disease, every country around the world spends trillions of dollars on disease management. Some of the recent solutions are based on the utilization of computer vision techniques in plant science which helps to monitor crop industries such as tomato, maize, grape, citrus, potato and cassava, and other crops. The attention-based CNN network has become effective in plant disease prediction. However, existing approaches are less precise in detecting minute-scale disease in the leaves. Our proposed Channel–Spatial segmentation network will help to determine the disease in the leaf, and it consists of two main stages: (a) channel attention discriminates diseased and healthy parts as well as channel-focused features, and (b) spatial attention consumes channel-focused features and highlights the diseased part for the final prediction process. This investigation forms a channel and spatial attention in a sequential way to identify diseased and healthy leaves. Finally, identified leaf diseases are divided into Mild, Medium, Severe, and Healthy. Our model successfully predicts the diseased leaves with the highest accuracy of 99.76%. Our research study shows evaluation metrics, comparison studies, and expert analysis to comprehend the network performance. This concludes that the Channel–Spatial segmentation network can be used effectively to diagnose different disease degrees based on a combination of image processing and statistical calculation.

A novel technique for leaf disease classification using Legion Kernels with parallel support vector machine (LK-PSVM) and fuzzy C means image segmentation

“…For instance, pine trees in USA are largely affected by little leave disease and have caused severe shortage in production for the last six years [ 4 ]. ML based techniques are quicker and more reliable and requires less effort and is more accurate [ 5 ].Image processing is used to assess the extent of damage caused by the disease and identifies color variations to mark the affected area. To divide an image into distinct sections, the two important functions, the segmentation and grouping needs to be carried out.…”

Identification of Maize Leaf Diseases Based On AlexNet and ResNet50 Convolutional Neural Networks

“…The identification of maize diseases can be achieved using various parts of the crop, but the simplest and most common method is visual analysis of symptomatic leaves (7). Early detection of crop disease plays a crucial role in enabling farmers to implement necessary control measures, including selecting appropriate pesticides, thereby increasing crop yield and improving overall quality (8,9).…”

ATRLeNet: A Deep Learning Model for Enhanced Classification of Oryza Sativa Pathologies

“…The quality of the output post pre-processing significantly influences the effectiveness of Oryza Sativa plant disease identification [9]. Given the variations in disease distribution, appearance, and texture, along with the tendency of images to darken from the center outward, disease localization classifiers utilizing adaptive boosting have proven effective.…”