Plant disease detection using a hybrid dilated CNN with attention mechanisms and optimized mask RCNN segmentation

In accordance with human life, agriculture has main role in it, and in addition to that most people are involved in some kind of agricultural activity either in a direct or indirect manner. Moreover, the agricultural sectors acquired a major role in supplying better quality food and thus made the greatest attribution to the growth of populations and economics. But, the disease over the crop has influenced the growth of the corresponding species and thus requires an earlier diagnosis of plant disease by utilizing the most adequate and automatic detection approach for improving the quality of the production of food as well as to reduce the loss in economic. But, there are no techniques in the conventional system for identifying the disease in diverse crops in the agricultural environment. In modern times, deep learning approaches have acquired tremendous enhancement in the identification of image categorization as well as the object detection system. For precise detection of plant disease, an improved classification model is developed. Initially, from the standard publicly available database, the images of the plants are aggregated. The gathered images are segmented using Dilated, Adaptive, and Attention-based Mask Recurrent Convolutional Neural Networks (DAA-MRCNN). Then, it is fed into a hybrid classification phase, where the new model namely Dilated, Adaptive, and Attention-based Multiscale DenseNet termed as (DAA-MDeNet) for classification. The classifier performance is improved by optimizing the parameter in Mask RCNN and Multiscale DenseNet using the hybrid optimization algorithm named African Vulture and Lemur Optimizer (AVLO). When compared with the other model, a superior performance is shown in the proposed model.