LCDDN-YOLO: Lightweight Cotton Disease Detection in Natural Environment, Based on Improved YOLOv8
To address the challenges of detecting cotton pests and diseases in natural environments, as well as the similarities in the features exhibited by cotton pests and diseases, a Lightweight Cotton Disease Detection in Natural Environment (LCDDN-YOLO) algorithm is proposed. The LCDDN-YOLO algorithm is...
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MDPI AG
2025-02-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/4/421 |
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| author | Haoran Feng Xiqu Chen Zhaoyan Duan |
| author_facet | Haoran Feng Xiqu Chen Zhaoyan Duan |
| author_sort | Haoran Feng |
| collection | DOAJ |
| description | To address the challenges of detecting cotton pests and diseases in natural environments, as well as the similarities in the features exhibited by cotton pests and diseases, a Lightweight Cotton Disease Detection in Natural Environment (LCDDN-YOLO) algorithm is proposed. The LCDDN-YOLO algorithm is based on YOLOv8n, and replaces part of the convolutional layers in the backbone network with Distributed Shift Convolution (DSConv). The BiFPN network is incorporated into the original architecture, adding learnable weights to evaluate the significance of various input features, thereby enhancing detection accuracy. Furthermore, it integrates Partial Convolution (PConv) and Distributed Shift Convolution (DSConv) into the C2f module, called PDS-C2f. Additionally, the CBAM attention mechanism is incorporated into the neck network to improve model performance. A Focal-EIoU loss function is also integrated to optimize the model’s training process. Experimental results show that compared to YOLOv8, the LCDDN-YOLO model reduces the number of parameters by 12.9% and the floating-point operations (FLOPs) by 9.9%, while precision, mAP@50, and recall improve by 4.6%, 6.5%, and 7.8%, respectively, reaching 89.5%, 85.4%, and 80.2%. In summary, the LCDDN-YOLO model offers excellent detection accuracy and speed, making it effective for pest and disease control in cotton fields, particularly in lightweight computing scenarios. |
| format | Article |
| id | doaj-art-81bcac94084a4fcfa4d128e92d8acd8a |
| institution | DOAJ |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-81bcac94084a4fcfa4d128e92d8acd8a2025-08-20T02:44:51ZengMDPI AGAgriculture2077-04722025-02-0115442110.3390/agriculture15040421LCDDN-YOLO: Lightweight Cotton Disease Detection in Natural Environment, Based on Improved YOLOv8Haoran Feng0Xiqu Chen1Zhaoyan Duan2School of Electric & Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, ChinaSchool of Electric & Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, ChinaSchool of Electric & Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, ChinaTo address the challenges of detecting cotton pests and diseases in natural environments, as well as the similarities in the features exhibited by cotton pests and diseases, a Lightweight Cotton Disease Detection in Natural Environment (LCDDN-YOLO) algorithm is proposed. The LCDDN-YOLO algorithm is based on YOLOv8n, and replaces part of the convolutional layers in the backbone network with Distributed Shift Convolution (DSConv). The BiFPN network is incorporated into the original architecture, adding learnable weights to evaluate the significance of various input features, thereby enhancing detection accuracy. Furthermore, it integrates Partial Convolution (PConv) and Distributed Shift Convolution (DSConv) into the C2f module, called PDS-C2f. Additionally, the CBAM attention mechanism is incorporated into the neck network to improve model performance. A Focal-EIoU loss function is also integrated to optimize the model’s training process. Experimental results show that compared to YOLOv8, the LCDDN-YOLO model reduces the number of parameters by 12.9% and the floating-point operations (FLOPs) by 9.9%, while precision, mAP@50, and recall improve by 4.6%, 6.5%, and 7.8%, respectively, reaching 89.5%, 85.4%, and 80.2%. In summary, the LCDDN-YOLO model offers excellent detection accuracy and speed, making it effective for pest and disease control in cotton fields, particularly in lightweight computing scenarios.https://www.mdpi.com/2077-0472/15/4/421deep learningcotton pests and diseaseslightweight modelC2f |
| spellingShingle | Haoran Feng Xiqu Chen Zhaoyan Duan LCDDN-YOLO: Lightweight Cotton Disease Detection in Natural Environment, Based on Improved YOLOv8 Agriculture deep learning cotton pests and diseases lightweight model C2f |
| title | LCDDN-YOLO: Lightweight Cotton Disease Detection in Natural Environment, Based on Improved YOLOv8 |
| title_full | LCDDN-YOLO: Lightweight Cotton Disease Detection in Natural Environment, Based on Improved YOLOv8 |
| title_fullStr | LCDDN-YOLO: Lightweight Cotton Disease Detection in Natural Environment, Based on Improved YOLOv8 |
| title_full_unstemmed | LCDDN-YOLO: Lightweight Cotton Disease Detection in Natural Environment, Based on Improved YOLOv8 |
| title_short | LCDDN-YOLO: Lightweight Cotton Disease Detection in Natural Environment, Based on Improved YOLOv8 |
| title_sort | lcddn yolo lightweight cotton disease detection in natural environment based on improved yolov8 |
| topic | deep learning cotton pests and diseases lightweight model C2f |
| url | https://www.mdpi.com/2077-0472/15/4/421 |
| work_keys_str_mv | AT haoranfeng lcddnyololightweightcottondiseasedetectioninnaturalenvironmentbasedonimprovedyolov8 AT xiquchen lcddnyololightweightcottondiseasedetectioninnaturalenvironmentbasedonimprovedyolov8 AT zhaoyanduan lcddnyololightweightcottondiseasedetectioninnaturalenvironmentbasedonimprovedyolov8 |