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The tea industry, as one of the most globally important agricultural products, is characterized by pests and diseases that pose a serious threat to yield and quality. These diseases and pests often present different scales and morphologies, and some pest and disease target sizes can be tiny and difficult to detect. To solve these problems, we propose TeaViTNet, a multi-scale attention-based tea pest and disease detection model that combines CNNs and Transformers. First, MobileViT is used as the feature extraction backbone network. MobileViT captures and analyzes the tiny pest and disease features in the image via a self-attention mechanism and global feature extraction. Second, the EMA-PANet network is introduced to optimize the model's learning and attention to the Apolygus lucorum and leaf blight regions via an efficient multi-scale attention module with cross-space learning, which improves the model's ability to understand multi-scale information. In addition, RFBNet is embedded in the module to further expand the perceptual range and effectively capture the information of tiny features in tea leaf images. Finally, the ODCSPLayer convolutional block is introduced, aiming to focus on acquiring richer gradient flow information. The experimental results show that the TeaViTNet model proposed in this paper has an average accuracy of 89.1%, which is a significant improvement over the baseline network MobileViT and is capable of accurately detecting Apolygus lucorum and leaf blight of different scales and complexities. [ABSTRACT FROM AUTHOR]

Background: The medical profession is facing an excessive workload, which has led to the development of various Computer-Aided Diagnosis (CAD) systems as well as Mobile-Aid Diagnosis (MAD) systems. These technologies enhance the speed and accuracy of diagnoses, particularly in areas with limited resources or remote regions during the pandemic. The primary purpose of this research is to predict and diagnose COVID-19 infection from chest X-ray images by developing a mobile-friendly deep learning framework, which has the potential for deployment in portable devices such as mobile or tablet, especially in situations where the workload of radiology specialists may be high. Moreover, this could improve the accuracy and transparency of population screening to assist radiologists during the pandemic. Methods: In this study, the Mobile Networks ensemble model called COV-MobNets is proposed to classify positive COVID-19 X-ray images from negative ones and can have an assistant role in diagnosing COVID-19. The proposed model is an ensemble model, combining two lightweight and mobile-friendly models: MobileViT based on transformer structure and MobileNetV3 based on Convolutional Neural Network. Hence, COV-MobNets can extract the features of chest X-ray images in two different methods to achieve better and more accurate results. In addition, data augmentation techniques were applied to the dataset to avoid overfitting during the training process. The COVIDx-CXR-3 benchmark dataset was used for training and evaluation. Results: The classification accuracy of the improved MobileViT and MobileNetV3 models on the test set has reached 92.5% and 97%, respectively, while the accuracy of the proposed model (COV-MobNets) has reached 97.75%. The sensitivity and specificity of the proposed model have also reached 98.5% and 97%, respectively. Experimental comparison proves the result is more accurate and balanced than other methods. Conclusion: The proposed method can distinguish between positive and negative COVID-19 cases more accurately and quickly. The proposed method proves that utilizing two automatic feature extractors with different structures as an overall framework of COVID-19 diagnosis can lead to improved performance, enhanced accuracy, and better generalization to new or unseen data. As a result, the proposed framework in this study can be used as an effective method for computer-aided diagnosis and mobile-aided diagnosis of COVID-19. The code is available publicly for open access at https://github.com/MAmirEshraghi/COV-MobNets. [ABSTRACT FROM AUTHOR]

Weeds in the field are serious threats to agricultural production. The rapid and accurate identification of weed species can be critical steps to automated weed control. However, previous weed recognition relies mainly on hand-designed features, such as shape and texture. Fortunately, computer vision can be expected to serve as intelligent weed control equipment. The performance can also be obtained better on the data samples with significant morphological differences. But it cannot accurately identify the weeds and crops in field environments. It is highly required to promote the expression capabilities of the weed species during recognition in real time, due to the particularity of field weeding tasks. In this study, a novel weed recognition was proposed to balance the high accuracy and real-time performance using the improved lightweight MobileViT network. The efficient Channel Attention (ECA) module was also combined to enhance the important parts of the features for better extraction. Firstly, the MobileViT network structure was selected as the lightweight model using Transformers. Specifically, the MobileViT backbone network was used to replace the self-attention layer in the ViT model with the lightweight separable convolution layer. Some optimization techniques were also introduced, such as the depth separable convolution and channel attention mechanism. The feature extraction was then achieved at a high inference speed, but a lower model size and computational cost. As such, the core network was established for lightweight weed identification. Secondly, the ECA mechanism was used as the learnable channel weights to calculate the importance of each channel, with emphasis on the important channels in the input features. The important features were enhanced to further improve the extraction capability from the down-sampled map. Finally, the MobileViT module was trained to simultaneously learn both local and global semantic information in the feature extraction network using both MobileViT and MobileNet networks. Only a small number of modules were combined to accurately capture the subtle differences between the different classes of weeds and crops, in order to fully meet the requirements of real-time performance and recognition accuracy. The comparative experiments were conducted to verify the effectiveness of the model. The images of corn seedlings and four types of companion weeds were also collected in the field environment. The results show that the recognition accuracy, precision, recall, and F1 score of the improved model were 99.61%, 99.60%, 99.58%, and 99.59%, respectively, indicating better performance than the lightweight convolutional neural networks (CNNs), such as ShuffleNet and MobileNet, and the general CNNs, such as VGG-16, ResNet-50, and DenseNet-161. Furthermore, the inference time was only 83ms for the single image, indicating that the recognition efficiency fully met the real-time requirement for the weed identification during weeding operations. The visualization also showed that the key features were effectively extracted from the weed images without the influence of background areas. The new model can be expected to accurately and rapidly distinguish the multiple morphologically similar weeds and crops in a field environment. The finding can also provide a strong reference for the weed identification systems in intelligent weed control equipment. [ABSTRACT FROM AUTHOR]

Recently, deep learning methods that use a combination of convolutional neural networks and Transformers have shown excellent results in both completely supervised and semi-supervised medical image segmentation tasks. This study provides a simple and effective framework for semi-supervised medical image segmentation by introducing cross teaching between MobileNet and MobileViT. Specifically, we built two different two-path parallel semantic segmentation networks with MobileNet and MobileViT as the main modules. Cross teaching between MobileNet and MobileViT was performed, in which the prediction of each network was used as a pseudo-label to supervise the training of other networks in a direct end-to-end manner. Finally, experiments on two common benchmark tests showed that the proposed framework was superior to six existing semi-supervised learning methods. This shows that our framework can effectively use unlabeled data to improve the performance and is superior to the latest semi-supervised segmentation method. Codes are available at: github.com/yywbkn/MV2-cross-teaching-MobileViT. • A simple and effective semi-supervised scheme, based on MobileNet and MobileViT. • A new cross teaching framework for medical image segmentation. • This framework performance reaches the level of the state-of-art semi-supervised scheme. [ABSTRACT FROM AUTHOR]