Pine wilt disease is extremely ruinous to forests. It is an important to hold back the transmission of the disease in order to detect diseased trees on UAV imagery, by using a detection algorithm. However, most of the existing detection algorithms for diseased trees ignore the interference of complex backgrounds to the diseased tree feature extraction in drone images. Moreover, the sampling range of the positive sample does not match the circular shape of the diseased tree in the existing sampling methods, resulting in a poor-quality positive sample of the sampled diseased tree. This paper proposes a Global Multi-Scale Channel Adaptation Network to solve these problems. Specifically, a global multi-scale channel attention module is developed, which alleviates the negative impact of background regions on the model. In addition, a center circle sampling method is proposed to make the sampling range of the positive sample fit the shape of a circular disease tree target, enhancing the positive sample’s sampling quality significantly. The experimental results show that our algorithm exceeds the seven mainstream algorithms on the diseased tree dataset, and achieves the best detection effect. The average precision (AP) and the recall are 79.8% and 86.6%, respectively.

Unmanned surface vessel (USV) target detection algorithms often face challenges such as misdetection and omission of small targets due to significant variations in target scales and susceptibility to interference from complex environments. To address these issues, we propose a small target enhanced YOLOv7 (STE-YOLO) approach. Firstly, we introduce a specialized detection branch designed to identify tiny targets. This enhancement aims to improve the multi-scale target detection capabilities and address difficulties in recognizing targets of different sizes. Secondly, we present the lite visual center (LVC) module, which effectively fuses data from different levels to give more attention to small targets. Additionally, we integrate the lite efficient layer aggregation networks (L-ELAN) into the backbone network to reduce redundant computations and enhance computational efficiency. Lastly, we use Wise-IOU to optimize the loss function definition, thereby improving the model robustness by dynamically optimizing gradient contributions from samples of varying quality. We conducted experiments on the WSODD dataset and the FIOW-Img dataset. The results on the comprehensive WSODD dataset demonstrate that STE-YOLO, when compared to YOLOv7, reduces network parameters by 14% while improving AP50 and APs scores by 2.1% and 1.6%, respectively. Furthermore, when compared to five other leading target detection algorithms, STE-YOLO demonstrates superior accuracy and efficiency.

Pine wild disease poses a serious threat to the ecological environment of national forests. Combining the object detection algorithm with Unmanned Aerial Vehicles (UAV) to detect pine wild diseased trees (PWDT) is a significant step in preventing the spread of pine wild disease. To address the issue of shallow feature layers lacking the ability to fully extract features from small-sized diseased trees in existing detection algorithms, as well as the problem of a small number of small-sized diseased trees in a single image, a Shallow Pooled Weighted Feature Enhancement Network (SPW-FEN) based on Small Target Expansion (STE) has been proposed for detecting PWDT. First, a Pooled Weighted Channel Attention (PWCA) module is presented and introduced into the shallow feature layer with rich small target information to enhance the network’s expressive ability regarding the characteristics of two-layer shallow feature maps. Additionally, an STE data enhancement method is introduced for small-sized targets, which effectively increases the sample size of small-sized diseased trees in a single image. The experimental results on the PWDT dataset indicate that the proposed algorithm achieved an average precision and recall of 79.1% and 86.9%, respectively. This is 3.6 and 3.8 percentage points higher, respectively, than the recognition recall and average precision of the existing state-of-the-art method Faster-RCNN, and 6.4 and 5.5 percentage points higher than those of the newly proposed YOLOv6 method.

Power consumers can obtain authoritative green environmental value certification through green electricity trading, which plays an important role in improving the production competitiveness of enterprises, especially for international product trade affairs. However, the credibility of green electricity transactions faces serious challenges in the enterprise green authentication affairs, especially the user’s identity authentication, the traceability of green electricity transactions, and the standardization of green electricity transactions. Aiming to solve the certification and traceability problem of tradable green certificates, this paper proposes an integrated green certificate trading protocol, which solves its double-trading problem and helps to improve the credibility of renewable energy use. The main contribution is providing a solution based on the consortium blockchain technology to solve the main challenges mentioned above. The main solved scheme designs a series of protocols, which includes a purchase protocol, payment protocol, and non-transferable protocol. The whole process ensures the credibility, traceability, and non-transferability of green certificate trading. Multiple verification measures are adopted to address security and privacy challenges in green certificate management. Through security analysis, the protocol effectively defends against attacks such as double payments, transaction rollback, and transaction replays while ensuring users’ privacy.

Recently, the DisentGAN unsupervised learning framework has made significant progress in remote sensing image cloud removal. However, most existing DisentGAN-based methods fail to effectively disentangle surface and cloud information within the latent space, leading to suboptimal performance in cloud removal. Moreover, the limited feature representation capabilities of existing haze and cloud removal methods frequently result in inadequate restoration of image texture details and colors. To address these issues, we propose a Latent Feature Disentanglement Bidirectional Prompting Network (LFDBP-Net) for unsupervised cloud removal. Specifically, we propose an Unsupervised Latent Feature Disentanglement (ULFD) framework that explicitly separates surface and cloud information by leveraging unpaired cloud-free latent features as priors to pull surface features closer and push cloud features apart, constrained by a bi-halfcycle reconstruction branch. Furthermore, the Bidirectional Prompt-guided Codec Mechanism (BPCM) enables coarse-to-fine feature reconstruction through a two-phase “Look and Think” process, in which the Residual Query State Space Model (RQSSM) modulates encoder-decoder queries using residual-guided historical hidden state to fuse complementary local structures and global context. The BPCM can progressively enhance feature discriminability and semantic expressiveness, thereby improving image textures and colors. Finally, we construct an all-season covered, difficulty-graded cloud removal dataset, named CSRD-CR. Experiments on both our proposed dataset and public datasets demonstrate our LFDBP-Net outperforms several state-of-the-art unsupervised haze and cloud removal methods. The source code and dataset are available at https://github.com/nbhuangzhixuan/LFDBP-Net.