Introduction: Current detection methods for apple leaf diseases still suffer some challenges, such as the high number of parameters, low detection speed and poor detection performance for small dense spots, which limit the practical applications in agriculture. Therefore, an efficient and accurate model for apple leaf disease detection based on YOLOv5 is proposed and named EADD-YOLO.Methods: In the EADD-YOLO, the lightweight shufflenet inverted residual module is utilized to reconstruct the backbone network, and an efficient feature learning module designed through depthwise convolution is proposed and introduced to the neck network. The aim is to reduce the number of parameters and floating point of operations (FLOPs) during feature extraction and feature fusion, thus increasing the operational efficiency of the network with less impact on detection performance. In addition, the coordinate attention module is embedded into the critical locations of the network to select the critical spot information and suppress useless information, which is to enhance the detection accuracy of diseases with various sizes from different scenes. Furthermore, the SIoU loss replaces CIoU loss as the bounding box regression loss function to improve the accuracy of prediction box localization.Results: The experimental results indicate that the proposed method can achieve the detection performance of 95.5% on the mean average precision and a speed of 625 frames per second (FPS) on the apple leaf disease dataset (ALDD). Compared to the latest research method on the ALDD, the detection accuracy and speed of the proposed method were improved by 12.3% and 596 FPS, respectively. In addition, the parameter quantity and FLOPs of the proposed method were much less than other relevant popular algorithms.Discussion: In summary, the proposed method not only has a satisfactory detection effect, but also has fewer parameters and high calculation efficiency compared with the existing approaches. Therefore, the proposed method provides a high-performance solution for the early diagnosis of apple leaf disease and can be applied in agricultural robots. The code repository is open-sourced at https://github.com/AWANWY/EADD-YOLO.

Contrastive self-supervised learning (CSSL) has achieved promising results in extracting visual features from unlabeled data. Most of the current CSSL methods are used to learn global image features with low-resolution that are not suitable or efficient for pixel-level tasks. In this paper, we propose a coarse-to-fine CSSL framework based on a novel contrasting strategy to address this problem. It consists of two stages, one for encoder pre-training to learn global features and the other for decoder pre-training to derive local features. Firstly, the novel contrasting strategy takes advantage of the spatial structure and semantic meaning of different regions and provides more cues to learn than that relying only on data augmentation. Specifically, a positive pair is built from two nearby patches sampled along the direction of the texture if they fall into the same cluster. A negative pair is generated from different clusters. When the novel contrasting strategy is applied to the coarse-to-fine CSSL framework, global and local features are learned successively by forcing the positive pair close to each other and the negative pair apart in an embedding space. Secondly, a discriminant constraint is incorporated into the per-pixel classification model to maximize the inter-class distance. It makes the classification model more competent at distinguishing between different categories that have similar appearance. Finally, the proposed method is validated on four SAR images for land-cover classification with limited labeled data and substantially improves the experimental results. The effectiveness of the proposed method is demonstrated in pixel-level tasks after comparison with the state-of-the-art methods.

Remote sensing image super-resolution (RSISR) techniques play a crucial role in various remote sensing applications. However, deep learning-based methods applied to RSISR encounter difficulties in learning complex features of remote sensing images and modeling long-term correlations between pixels. This study proposes a multiscale sliding window attention generation adversarial network (MSWAGAN), which combines the advantages of convolutional neural networks (CNNs) and transformers to overcome these limitations. The MSWAGAN consists of three main parts. In the shallow feature extraction part, CNN is used to extract shallow features from remote sensing images. The deep feature extraction part is divided into two stages. First, a multiscale sliding window attention (MSWA) is designed to replace the multihead attention (MHA) in the transformer. MSWA can learn local multiscale complex features of remote sensing images without increasing the number of parameters in MHA. Then, the transformer is utilized to learn global image features and model the long-range correlations between pixels. The image reconstruction part utilizes subpixel convolution for feature upsampling. Furthermore, in order to extend the application of super-resolution (SR) remote sensing images, a cross-sensor real multispectral RSISR dataset consisting of Landsat-8 (L8) and Sentinel-2 (S2) images was constructed, and a series of experiments to improve the spatial resolution of L8 images from 30 to 10 m in B, G, R, and near-infrared (NIR) bands were conducted. Experimental results demonstrate that our method outperforms some of the latest SR methods.

With the development of deep learning technology, the application of convolutional neural network (CNN) and vision transformer (ViT) for polarimetric synthetic aperture radar (PolSAR) image classification has been deepened. However, the PolSAR image has very rich information due to its special data form, which makes it difficult for the existing single network structure to comprehensively extract such effective information. In addition, deep learning methods require a large amount of data for training, whereas PolSAR labeled data is scarce and difficult to obtain. Therefore, a multi-granularity hybrid CNN-ViT model based on external tokens and cross-attention is proposed for PolSAR image classification. First of all, CNN is able to learn local features very well. Thus, a CNN-based external feature extractor is designed to extract local features from the PolSAR image. Then, ViT can focus on global features. So, a multi-granularity attention structure is constructed for extracting global information at multiple scales. With these two modules, the model can fully access the feature information contained in PolSAR images, which is more advantageous than a single network structure. Next, to further utilize these features, a crossattention feature fusion module is built for fusing global-local information of different granularities. Finally, by connecting with the softmax classifier, the network outputs the final prediction results. Experimental results on three benchmark datasets show that the present method using a small amount of labeled data for training also achieves the highest level of classification among the compared methods.

Introduction: Early detection of leaf diseases is necessary to control the spread of plant diseases, and one of the important steps is the segmentation of leaf and disease images. The uneven light and leaf overlap in complex situations make segmentation of leaves and diseases quite difficult. Moreover, the significant differences in ratios of leaf and disease pixels results in a challenge in identifying diseases.Methods: To solve the above issues, the residual attention mechanism combined with atrous spatial pyramid pooling and weight compression loss of UNet is proposed, which is named RAAWC-UNet. Firstly, weights compression loss is a method that introduces a modulation factor in front of the cross-entropy loss, aiming at solving the problem of the imbalance between foreground and background pixels. Secondly, the residual network and the convolutional block attention module are combined to form Res_CBAM. It can accurately localize pixels at the edge of the disease and alleviate the vanishing of gradient and semantic information from downsampling. Finally, in the last layer of downsampling, the atrous spatial pyramid pooling is used instead of two convolutions to solve the problem of insufficient spatial context information.Results: The experimental results show that the proposed RAAWC-UNet increases the intersection over union in leaf and disease segmentation by 1.91% and 5.61%, and the pixel accuracy of disease by 4.65% compared with UNet.Discussion: The effectiveness of the proposed method was further verified by the better results in comparison with deep learning methods with similar network architectures.