Xingchao Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Xingchao Zhang has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Chenxu Dai
College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
1 shared publication - Jinyun Liu
College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
1 shared publication - Li Li
College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
1 shared publication - Zhanlin Ji
College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, China
1 shared publication - Zhihua Bian
Department of Information Engineering, Hebei University of Environmental Engineering, Qinhuangdao 066102, China
1 shared publication
Latest Publications
- Open Access
RDL-YOLO: A Method for the Detection of Leaf Pests and Diseases in Cotton Based on YOLOv11
Accurate identification of cotton leaf pests and diseases is essential for sustainable cultivation but is challenged by complex backgrounds, diverse pest morphologies, and varied symptoms, where existing deep learning models often show insufficient robustness. To address these challenges, RDL-YOLO model is proposed in this study. In the proposed model, RepViT-Atrous Convolution (RepViT-A) is employed as the backbone network to enhance local–global interaction and improve the response intensity and extraction accuracy of key lesion features. In addition, the Dilated Dense Convolution (DDC) module is designed to achieve a dynamic multi-scale receptive field, enabling the network to adapt to lesion defects of different shapes and sizes. LDConv further optimizes the effect of feature fusion. Experimental results showed that the mean Average Precision (mAP) of the proposed model reached 77.1%, representing a 3.7% improvement over the baseline YOLOv11. Compared with leading detectors such as Real-Time Detection Transformer (RT-DETR), You Only Look Once version 11 (YOLOv11), DETRs as Fine-grained Distribution Refinement (D-FINE), and Spatial Transformer Network-YOLO (STN-YOLO). RDL-YOLO exhibits superior performance, enhanced reliability, and strong generalization capabilities in tests on the cotton leaf dataset and public datasets. This advancement offers a practical technical solution for improved agricultural pest and disease management.
Open AccessRDL-YOLO: A Method for the Detection of Leaf Pests and Diseases in Cotton Based on YOLOv11
Accurate identification of cotton leaf pests and diseases is essential for sustainable cultivation but is challenged by complex backgrounds, diverse pest morphologies, and varied symptoms, where existing deep learning models often show insufficient robustness. To address these challenges, RDL-YOLO model is proposed in this study. In the proposed model, RepViT-Atrous Convolution (RepViT-A) is employed as the backbone network to enhance local–global interaction and improve the response intensity and extraction accuracy of key lesion features. In addition, the Dilated Dense Convolution (DDC) module is designed to achieve a dynamic multi-scale receptive field, enabling the network to adapt to lesion defects of different shapes and sizes. LDConv further optimizes the effect of feature fusion. Experimental results showed that the mean Average Precision (mAP) of the proposed model reached 77.1%, representing a 3.7% improvement over the baseline YOLOv11. Compared with leading detectors such as Real-Time Detection Transformer (RT-DETR), You Only Look Once version 11 (YOLOv11), DETRs as Fine-grained Distribution Refinement (D-FINE), and Spatial Transformer Network-YOLO (STN-YOLO). RDL-YOLO exhibits superior performance, enhanced reliability, and strong generalization capabilities in tests on the cotton leaf dataset and public datasets. This advancement offers a practical technical solution for improved agricultural pest and disease management.