DCNet: DenseNet-77-based CornerNet model for the tomato plant leaf disease detection and classification - Albahli, Saleh; Nawaz, Marriam (Frontiers in Plant Science, 2022)
The earliest recognition of tomato plant leaf diseases is mandatory to improve the food yield and save agriculturalists from costly spray procedures. The correct and timely identification of several tomato plant leaf diseases is a complicated task as there exists an immense similarity between the healthy and affected areas of plant leaves. Moreover, the incidence of light variation, color, and brightness changes, and the occurrence of blurring and noise on the images further increase the complexity of the detection process. In this work, we have presented a robust approach to tackle the existing issues of tomato plant leaf disease detection and classification by using deep learning. We have proposed a novel approach namely the DenseNet-77-based CornerNet model for the localization and classification of the tomato plant leaf abnormalities. Specifically, we have used the DenseNet-77 as the backbone network of the CornerNet which assists to compute the more nominative set of image features from the suspected samples which are later categorized into ten classes by the one-stage detector of the CornerNet model. We have evaluated the proposed solution on a standard dataset named the PlantVillage which is challenging in nature as it contains the samples with immense brightness alterations, color variations, and leaf images with different dimensions and shapes. We have conducted several experiments to assure the effectiveness of our approach for the timely recognition of the tomato plant leaf diseases that can assist the agriculturalist to replace the manual systems.