Plant leaves disease detection in complex background using automatic segmentation and improved multi-scale feature fusion network

In order to solve a number of issues and increase agricultural output, researchers have recently widely used Artificial Intelligence (AI) approaches in smart farming. Given the enormous diversity of plants in the world and the many diseases that have a negative effect on crop productivity, identifying and categorizing plant diseases is a difficult undertaking. One of the main areas of research globally has been the automatic segmentation of images showing plant leaf diseases. The efficiency of the proposed model is assessed using open-source datasets, such as FGVC 7, which comprise four distinct classes of leaf diseases. The picture undergoes three phases of pre-processing. First is, a Rank Order Fuzzy (ROF) filter approach is used to minimize background noise in the plant image. Then resizing the image and finally augment of data. The next step involves identifying disease spots using histogram-based methods based on the L*a*b* color model. Finding disease spots before segmentation helps ensure proper segmentation, which is the goal of the next stage, which involves dividing the leaf pictures into uniform areas. Furthermore, a fusion model for data categorization uses patches segmentation data as input. To improve the system's accuracy while operating independently, different leaf disease zones are segmented, and patches are created using patch segmentation algorithms. This proposed model utilizes the advantages of both Convolutional Neural Networks (CNNs) and Vision Transformers (ViT) to extract potent early features fusion by integrating CNN designs such as VGG16, InceptionV3, AlexNet, and Google Net. After that, local characteristics are captured using a ViT model to accurately detect plant illnesses. With an impressive accuracy of 99.85%, the proposed fusion model performs better than similar previously published methods in the detection and categorization of several plant leaf diseases.