Investigation of an Efficient Multi-Class Cotton Leaf Disease Detection Algorithm That Leverages YOLOv11
Cotton leaf diseases can lead to substantial yield losses and economic burdens. Traditional detection methods are challenged by low accuracy and high labor costs. This research presents the ACURS-YOLO network, an advanced cotton leaf disease detection architecture developed on the foundation of YOLO...
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2025-07-01
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| author | Fangyu Hu Mairheba Abula Di Wang Xuan Li Ning Yan Qu Xie Xuedong Zhang |
| author_facet | Fangyu Hu Mairheba Abula Di Wang Xuan Li Ning Yan Qu Xie Xuedong Zhang |
| author_sort | Fangyu Hu |
| collection | DOAJ |
| description | Cotton leaf diseases can lead to substantial yield losses and economic burdens. Traditional detection methods are challenged by low accuracy and high labor costs. This research presents the ACURS-YOLO network, an advanced cotton leaf disease detection architecture developed on the foundation of YOLOv11. By integrating a medical image segmentation model, it effectively tackles challenges including complex background interference, the missed detection of small targets, and restricted generalization ability. Specifically, the U-Net v2 module is embedded in the backbone network to boost the multi-scale feature extraction performance in YOLOv11. Meanwhile, the CBAM attention mechanism is integrated to emphasize critical disease-related features. To lower the computational complexity, the SPPF module is substituted with SimSPPF. The C3k2_RCM module is appended for long–range context modeling, and the ARelu activation function is employed to alleviate the vanishing gradient problem. A database comprising 3000 images covering six types of cotton leaf diseases was constructed, and data augmentation techniques were applied. The experimental results show that ACURS-YOLO attains impressive performance indicators, encompassing a mAP_0.5 value of 94.6%, a mAP_0.5:0.95 value of 83.4%, 95.5% accuracy, 89.3% recall, an F1 score of 92.3%, and a frame rate of 148 frames per second. It outperforms YOLOv11 and other conventional models with regard to both detection precision and overall functionality. Ablation tests additionally validate the efficacy of each component, affirming the framework’s advantage in addressing complex detection environments. This framework provides an efficient solution for the automated monitoring of cotton leaf diseases, advancing the development of smart sensors through improved detection accuracy and practical applicability. |
| format | Article |
| id | doaj-art-7e2be6bb8da4429abe8f6fd43b0a1d60 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-7e2be6bb8da4429abe8f6fd43b0a1d602025-08-20T03:32:18ZengMDPI AGSensors1424-82202025-07-012514443210.3390/s25144432Investigation of an Efficient Multi-Class Cotton Leaf Disease Detection Algorithm That Leverages YOLOv11Fangyu Hu0Mairheba Abula1Di Wang2Xuan Li3Ning Yan4Qu Xie5Xuedong Zhang6College of Information Engineering, Tarim University, Alaer 843300, ChinaCollege of Information Engineering, Tarim University, Alaer 843300, ChinaCollege of Information Engineering, Tarim University, Alaer 843300, ChinaCollege of Information Engineering, Tarim University, Alaer 843300, ChinaCollege of Information Engineering, Tarim University, Alaer 843300, ChinaCollege of Information Engineering, Tarim University, Alaer 843300, ChinaCollege of Information Engineering, Tarim University, Alaer 843300, ChinaCotton leaf diseases can lead to substantial yield losses and economic burdens. Traditional detection methods are challenged by low accuracy and high labor costs. This research presents the ACURS-YOLO network, an advanced cotton leaf disease detection architecture developed on the foundation of YOLOv11. By integrating a medical image segmentation model, it effectively tackles challenges including complex background interference, the missed detection of small targets, and restricted generalization ability. Specifically, the U-Net v2 module is embedded in the backbone network to boost the multi-scale feature extraction performance in YOLOv11. Meanwhile, the CBAM attention mechanism is integrated to emphasize critical disease-related features. To lower the computational complexity, the SPPF module is substituted with SimSPPF. The C3k2_RCM module is appended for long–range context modeling, and the ARelu activation function is employed to alleviate the vanishing gradient problem. A database comprising 3000 images covering six types of cotton leaf diseases was constructed, and data augmentation techniques were applied. The experimental results show that ACURS-YOLO attains impressive performance indicators, encompassing a mAP_0.5 value of 94.6%, a mAP_0.5:0.95 value of 83.4%, 95.5% accuracy, 89.3% recall, an F1 score of 92.3%, and a frame rate of 148 frames per second. It outperforms YOLOv11 and other conventional models with regard to both detection precision and overall functionality. Ablation tests additionally validate the efficacy of each component, affirming the framework’s advantage in addressing complex detection environments. This framework provides an efficient solution for the automated monitoring of cotton leaf diseases, advancing the development of smart sensors through improved detection accuracy and practical applicability.https://www.mdpi.com/1424-8220/25/14/4432YOLOv11U-Net v2cotton leaf disease detection |
| spellingShingle | Fangyu Hu Mairheba Abula Di Wang Xuan Li Ning Yan Qu Xie Xuedong Zhang Investigation of an Efficient Multi-Class Cotton Leaf Disease Detection Algorithm That Leverages YOLOv11 Sensors YOLOv11 U-Net v2 cotton leaf disease detection |
| title | Investigation of an Efficient Multi-Class Cotton Leaf Disease Detection Algorithm That Leverages YOLOv11 |
| title_full | Investigation of an Efficient Multi-Class Cotton Leaf Disease Detection Algorithm That Leverages YOLOv11 |
| title_fullStr | Investigation of an Efficient Multi-Class Cotton Leaf Disease Detection Algorithm That Leverages YOLOv11 |
| title_full_unstemmed | Investigation of an Efficient Multi-Class Cotton Leaf Disease Detection Algorithm That Leverages YOLOv11 |
| title_short | Investigation of an Efficient Multi-Class Cotton Leaf Disease Detection Algorithm That Leverages YOLOv11 |
| title_sort | investigation of an efficient multi class cotton leaf disease detection algorithm that leverages yolov11 |
| topic | YOLOv11 U-Net v2 cotton leaf disease detection |
| url | https://www.mdpi.com/1424-8220/25/14/4432 |
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