A lightweight weed detection model for cotton fields based on an improved YOLOv8n
Abstract In modern agriculture, the proliferation of weeds in cotton fields poses a significant threat to the healthy growth and yield of crops. Therefore, efficient detection and control of cotton field weeds are of paramount importance. In recent years, deep learning models have shown great potent...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84748-8 |
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| author | Jun Wang Zhengyuan Qi Yanlong Wang Yanyang Liu |
| author_facet | Jun Wang Zhengyuan Qi Yanlong Wang Yanyang Liu |
| author_sort | Jun Wang |
| collection | DOAJ |
| description | Abstract In modern agriculture, the proliferation of weeds in cotton fields poses a significant threat to the healthy growth and yield of crops. Therefore, efficient detection and control of cotton field weeds are of paramount importance. In recent years, deep learning models have shown great potential in the detection of cotton field weeds, achieving high-precision weed recognition. However, existing deep learning models, despite their high accuracy, often have complex computations and high resource consumption, making them difficult to apply in practical scenarios. To address this issue, developing efficient and lightweight detection methods for weed recognition in cotton fields is crucial for effective weed control. This study proposes the YOLO-Weed Nano algorithm based on the improved YOLOv8n model. First, the Depthwise Separable Convolution (DSC) structure is used to improve the HGNetV2 network, creating the DS_HGNetV2 network to replace the backbone of the YOLOv8n model. Secondly, the Bidirectional Feature Pyramid Network (BiFPN) is introduced to enhance the feature fusion layer, further optimizing the model’s ability to recognize weed features in complex backgrounds. Finally, a lightweight detection head, LiteDetect, suitable for the BiFPN structure, is designed to streamline the model structure and reduce computational load. Experimental results show that compared to the original YOLOv8n model, YOLO-Weed Nano improves mAP by 1%, while reducing the number of parameters, computation, and weights by 63.8%, 42%, and 60.7%, respectively. |
| format | Article |
| id | doaj-art-58a708b34abd4f789d92ad3cb38c6ca3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-58a708b34abd4f789d92ad3cb38c6ca32025-01-05T12:17:22ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-024-84748-8A lightweight weed detection model for cotton fields based on an improved YOLOv8nJun Wang0Zhengyuan Qi1Yanlong Wang2Yanyang Liu3College of Information Science and Technology, Gansu Agricultural UniversityCollege of Information Science and Technology, Gansu Agricultural UniversityCollege of Information Science and Technology, Gansu Agricultural UniversityCollege of Information Science and Technology, Gansu Agricultural UniversityAbstract In modern agriculture, the proliferation of weeds in cotton fields poses a significant threat to the healthy growth and yield of crops. Therefore, efficient detection and control of cotton field weeds are of paramount importance. In recent years, deep learning models have shown great potential in the detection of cotton field weeds, achieving high-precision weed recognition. However, existing deep learning models, despite their high accuracy, often have complex computations and high resource consumption, making them difficult to apply in practical scenarios. To address this issue, developing efficient and lightweight detection methods for weed recognition in cotton fields is crucial for effective weed control. This study proposes the YOLO-Weed Nano algorithm based on the improved YOLOv8n model. First, the Depthwise Separable Convolution (DSC) structure is used to improve the HGNetV2 network, creating the DS_HGNetV2 network to replace the backbone of the YOLOv8n model. Secondly, the Bidirectional Feature Pyramid Network (BiFPN) is introduced to enhance the feature fusion layer, further optimizing the model’s ability to recognize weed features in complex backgrounds. Finally, a lightweight detection head, LiteDetect, suitable for the BiFPN structure, is designed to streamline the model structure and reduce computational load. Experimental results show that compared to the original YOLOv8n model, YOLO-Weed Nano improves mAP by 1%, while reducing the number of parameters, computation, and weights by 63.8%, 42%, and 60.7%, respectively.https://doi.org/10.1038/s41598-024-84748-8Object detectionWeed detectionYOLOv8Deep learningLightweight modelCotton |
| spellingShingle | Jun Wang Zhengyuan Qi Yanlong Wang Yanyang Liu A lightweight weed detection model for cotton fields based on an improved YOLOv8n Scientific Reports Object detection Weed detection YOLOv8 Deep learning Lightweight model Cotton |
| title | A lightweight weed detection model for cotton fields based on an improved YOLOv8n |
| title_full | A lightweight weed detection model for cotton fields based on an improved YOLOv8n |
| title_fullStr | A lightweight weed detection model for cotton fields based on an improved YOLOv8n |
| title_full_unstemmed | A lightweight weed detection model for cotton fields based on an improved YOLOv8n |
| title_short | A lightweight weed detection model for cotton fields based on an improved YOLOv8n |
| title_sort | lightweight weed detection model for cotton fields based on an improved yolov8n |
| topic | Object detection Weed detection YOLOv8 Deep learning Lightweight model Cotton |
| url | https://doi.org/10.1038/s41598-024-84748-8 |
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