Plant disease can diminish a considerable portion of the agricultural products on each farm. The main goal of this work is to provide visual information for the farmers to enable them to take the necessary preventive measures. A lightweight deep learning approach is proposed based on the Vision Transformer (ViT) for real-time automated plant disease classification. In addition to the ViT, the classical convolutional neural network (CNN) methods and the combination of CNN and ViT have been implemented for the plant disease classification. The models have been trained and evaluated on multiple datasets. Based on the comparison between the obtained results, it is concluded that although attention blocks increase the accuracy, they decelerate the prediction. Combining attention blocks with CNN blocks can compensate for the speed.
During the COVID-19 pandemic, wearing a face mask has been an e ective way to prevent the spread of COVID-19. In a number of monitoring jobs, human workforce has been replaced with computers thanks to the outstanding performance of deep learning models. Monitoring the wearing of a face mask is another task that can be done by deep learning models with acceptable accuracy. The main challenge of this task, however, is the limited amount of data because of the quarantine constraints. This study investigated the capability of three state-of-the-art object detection neural networks to detect face mask for real-world applications. To this end, three models were employed: Single Shot Detector (SSD) and two versions of You Only Look Once (YOLO) including YOLOv4-tiny and YOLOv4-tiny-3l, among which the best was selected. In the proposed method, according to the performance of these models, the most viable model for real-world and mobile device applications, compared to other recent studies, was the YOLOv4-tiny model with the mean Average Precision (mAP) and Frames Per Second (FPS) of 85.31% and 50.66, respectively. These acceptable values were obtained using two datasets with only 1531 images in three separate classes.
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