A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
Existing methods for detecting cotton boll diseases frequently exhibit high rates of <b>both</b> false negatives and false positives under complex field conditions (e.g., lighting variations, shadows, and occlusions) and struggle to achieve real-time performance on edge devices. To addre...
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MDPI AG
2025-07-01
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| author | Lei Yang Wenhao Cui Jingqian Li Guotao Han Qi Zhou Yubin Lan Jing Zhao Yongliang Qiao |
| author_facet | Lei Yang Wenhao Cui Jingqian Li Guotao Han Qi Zhou Yubin Lan Jing Zhao Yongliang Qiao |
| author_sort | Lei Yang |
| collection | DOAJ |
| description | Existing methods for detecting cotton boll diseases frequently exhibit high rates of <b>both</b> false negatives and false positives under complex field conditions (e.g., lighting variations, shadows, and occlusions) and struggle to achieve real-time performance on edge devices. To address these limitations, this study proposes an enhanced YOLOv11n model (YOLOv11n-ECS) for improved detection accuracy. A dataset of cotton boll diseases under different lighting conditions and shooting angles in the field was constructed. To mitigate false negatives and false positives encountered by the original YOLOv11n model during detection, the EMA (efficient multi-scale attention) mechanism is introduced to enhance the weights of important features and suppress irrelevant regions, thereby improving the detection accuracy of the model. Partial Convolution (PConv) is incorporated into the C3k2 module to reduce computational redundancy and lower the model’s computational complexity while maintaining high recognition accuracy. Furthermore, to enhance the localization accuracy of diseased bolls, the original CIoU loss is replaced with Shape-IoU. The improved model achieves floating point operations (FLOPs), parameter count, and model size at 96.8%, 96%, and 96.3% of the original YOLOv11n model, respectively. The improved model achieves an mAP@0.5 of 85.6% and an mAP@0.5:0.95 of 62.7%, representing improvements of 2.3 and 1.9 percentage points, respectively, over the baseline YOLOv11n model. Compared with CenterNet, Faster R-CNN, YOLOv8-LSW, MSA-DETR, DMN-YOLO, and YOLOv11n, the improved model shows mAP@0.5 improvements of 25.7, 21.2, 5.5, 4.0, 4.5, and 2.3 percentage points, respectively, along with corresponding mAP@0.5:0.95 increases of 25.6, 25.3, 8.3, 2.8, 1.8, and 1.9 percentage points. Deployed on a Jetson TX2 development board, the model achieves a recognition speed of 56 frames per second (FPS) and an mAP of 84.2%, confirming its suitability for real-time detection. Furthermore, the improved model effectively reduces instances of both false negatives and false positives for diseased cotton bolls while yielding higher detection confidence, thus providing robust technical support for intelligent cotton boll disease detection. |
| format | Article |
| id | doaj-art-af536cc8f41f4ded97acf6048b523f6c |
| institution | DOAJ |
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| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-af536cc8f41f4ded97acf6048b523f6c2025-08-20T02:45:33ZengMDPI AGApplied Sciences2076-34172025-07-011514808510.3390/app15148085A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11nLei Yang0Wenhao Cui1Jingqian Li2Guotao Han3Qi Zhou4Yubin Lan5Jing Zhao6Yongliang Qiao7School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, ChinaSchool of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, ChinaSchool of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, ChinaSchool of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, ChinaSchool of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, ChinaSchool of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, ChinaSchool of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, ChinaAustralian Institute for Machine Learning (AIML), University of Adelaide, Adelaide, SA 5000, AustraliaExisting methods for detecting cotton boll diseases frequently exhibit high rates of <b>both</b> false negatives and false positives under complex field conditions (e.g., lighting variations, shadows, and occlusions) and struggle to achieve real-time performance on edge devices. To address these limitations, this study proposes an enhanced YOLOv11n model (YOLOv11n-ECS) for improved detection accuracy. A dataset of cotton boll diseases under different lighting conditions and shooting angles in the field was constructed. To mitigate false negatives and false positives encountered by the original YOLOv11n model during detection, the EMA (efficient multi-scale attention) mechanism is introduced to enhance the weights of important features and suppress irrelevant regions, thereby improving the detection accuracy of the model. Partial Convolution (PConv) is incorporated into the C3k2 module to reduce computational redundancy and lower the model’s computational complexity while maintaining high recognition accuracy. Furthermore, to enhance the localization accuracy of diseased bolls, the original CIoU loss is replaced with Shape-IoU. The improved model achieves floating point operations (FLOPs), parameter count, and model size at 96.8%, 96%, and 96.3% of the original YOLOv11n model, respectively. The improved model achieves an mAP@0.5 of 85.6% and an mAP@0.5:0.95 of 62.7%, representing improvements of 2.3 and 1.9 percentage points, respectively, over the baseline YOLOv11n model. Compared with CenterNet, Faster R-CNN, YOLOv8-LSW, MSA-DETR, DMN-YOLO, and YOLOv11n, the improved model shows mAP@0.5 improvements of 25.7, 21.2, 5.5, 4.0, 4.5, and 2.3 percentage points, respectively, along with corresponding mAP@0.5:0.95 increases of 25.6, 25.3, 8.3, 2.8, 1.8, and 1.9 percentage points. Deployed on a Jetson TX2 development board, the model achieves a recognition speed of 56 frames per second (FPS) and an mAP of 84.2%, confirming its suitability for real-time detection. Furthermore, the improved model effectively reduces instances of both false negatives and false positives for diseased cotton bolls while yielding higher detection confidence, thus providing robust technical support for intelligent cotton boll disease detection.https://www.mdpi.com/2076-3417/15/14/8085detection of cotton boll diseasesYOLOv11EMA mechanismpartial convolutionloss function |
| spellingShingle | Lei Yang Wenhao Cui Jingqian Li Guotao Han Qi Zhou Yubin Lan Jing Zhao Yongliang Qiao A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n Applied Sciences detection of cotton boll diseases YOLOv11 EMA mechanism partial convolution loss function |
| title | A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n |
| title_full | A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n |
| title_fullStr | A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n |
| title_full_unstemmed | A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n |
| title_short | A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n |
| title_sort | real time cotton boll disease detection model based on enhanced yolov11n |
| topic | detection of cotton boll diseases YOLOv11 EMA mechanism partial convolution loss function |
| url | https://www.mdpi.com/2076-3417/15/14/8085 |
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