Rui Mao

Fusarium head blight (FHB), caused by the Fusarium species complex, significantly endangers wheat yield and safety. Accurate and timely assessment of FHB epidemic level in the field is crucial for effective disease management. However, the complex environment and indistinct edges of diseased areas present substantial challenges in distinguishing between healthy and diseased ears, thereby impacting the accuracy of FHB epidemic level detection. This study proposes EBS-YOLO, a novel Edge-Optimized Bidirectional Spatial Feature Augmentation YOLO Network, specifically designed for the rapid and precise determination of FHB epidemic levels at the canopy level. The Focal-Edge Selection Module (FSM) within the backbone replaces original C2f module to enhance edge feature representation and facilitate multi-scale feature extraction. Furthermore, the Dual Spatial-Connection Feature Pyramid Network (DSCFPN), integrating Global-to-Local Spatial Aggregation (GLSA) with bidirectional pyramid interaction, balances global and local feature acquisition while optimizing the feature fusion mechanism. This design enables the model to effectively handle occlusions, scale variations, and complex environments. Experimental results demonstrate substantial improvements over eight comparative models in detecting healthy and diseased wheat ears, achieving mean Average Precision (mAP) of 86.1% and 82.9%, respectively. Notably, the model achieved a mean accuracy of 94.7% in detecting FHB epidemic levels through rigorous spatiotemporal validation using datasets collected from independent fields across different years, underscoring its robust generalization capability. Characterized by its low complexity and lightweight design, EBS-YOLO features a parameter count of 2.05 M, 7.4 GFLOPs, and a model size of 5.0 MB, making it an efficient approach for real-time FHB epidemic level detection.

Anti-CRISPR proteins play a crucial role in bacterial-phage interactions by inhibiting the CRISPR/Cas system and thus enhancing phage survival. Accurately predicting these proteins is essential for understanding phage-host immune interactions and progressing CRISPR/ Cas-based technologies. Current approaches primarily analyze proteins individually, which may overlook the intrinsic similarities and potential connections among protein sequences. This study introduces PACRGNN, a graph neural network framework that creates a heterogeneous protein network by integrating sequence and structural similarities, wherein nodes represent proteins and edges signify their relationships. By combining Graph Attention (GAT) and Graph Sample and Aggregation (GraphSAGE) layers, PACRGNN captures both local and global topological dependencies, while incorporating six protein feature categories to enrich node representations. PACRGNN achieves an accuracy of 0.9577, an F1-Score of 0.9572, and a PRAUC of 0.9876 on the validation set. The model demonstrated superior performance to existing methods on the independent test set derived from NCBI database (Jan.–Oct. 2024).

Livestock behavior serves as a crucial indicator of physiological health. Leveraging deep learning techniques to automatically recognize dairy goat behaviors, particularly abnormal ones, enables early detection of potential health and environmental issues. To address the challenges of recognizing small-target behaviors in complex environments, a multi-scale and lightweight behavior recognition model for dairy goats called GSCW-YOLO was proposed. The model integrates Gaussian Context Transformation (GCT) and the Content-Aware Reassembly of Features (CARAFE) upsampling operator, enhancing the YOLOv8n framework’s attention to behavioral features, reducing interferences from complex backgrounds, and improving the ability to distinguish subtle behavior differences. Additionally, GSCW-YOLO incorporates a small-target detection layer and optimizes the Wise-IoU loss function, increasing its effectiveness in detecting distant small-target behaviors and transient abnormal behaviors in surveillance videos. Data for this study were collected via video surveillance under varying lighting conditions and evaluated on a self-constructed dataset comprising 9213 images. Experimental results demonstrated that the GSCW-YOLO model achieved a precision of 93.5%, a recall of 94.1%, and a mean Average Precision (mAP) of 97.5%, representing improvements of 3, 3.1, and 2 percentage points, respectively, compared to the YOLOv8n model. Furthermore, GSCW-YOLO is highly efficient, with a model size of just 5.9 MB and a frame per second (FPS) of 175. It outperforms popular models such as CenterNet, EfficientDet, and other YOLO-series networks, providing significant technical support for the intelligent management and welfare-focused breeding of dairy goats, thus advancing the modernization of the dairy goat industry.

Livestock behavior serves as a crucial indicator of physiological health. Using deep learning methods to automatically recognize dairy goat behaviors, especially abnormal ones, can aid in the early detection of potential health and environmental issues. This study proposed a multi-scale and lightweight behavior recognition model for dairy goats, known as GSCW-YOLO, which integrates Gaussian Context Transformation (GCT) and the Content-Aware Reassembly of Features upsampling operator (CARAFE). This integration aims to enhance the YOLOv8n model's attention to behavioral features, mitigate complex background interferences and concurrently enhance the model's capability to discern subtle differences among behaviors. Furthermore, the model includes a small target detection layer and optimization of the Wise-IoU loss function to enhance its adaptability in identifying distant small target behaviors and transient abnormal behaviors in surveillance videos. A new dataset called GoatABRD was also established, encompassing four abnormal behaviors (limping, attacking, death and gnawing) and six common behaviors (standing, lying, eating, drinking, scratching and grooming). The experimental results demonstrate that the precision, recall and mean Average Precision (mAP) of the GSCW-YOLO model reached 93.5%, 94.1% and 97.5%, respectively, marking improvements of 3, 3.1 and 2 percentage points over the YOLOv8n model. Moreover, the GSCW-YOLO model has a considerably smaller size of only 5.9MB, with a frame per second (FPS) of 175. Meanwhile, it outperforms popular models like CenterNet, Efficientdet, and other networks in the YOLO series. GSCW-YOLO provides significant technical support for the intelligent management and welfare-focused breeding of dairy goats, thereby facilitating the modernization of the dairy goat industry.