Citrus Disease Detection Based on Dilated Reparam Feature Enhancement and Shared Parameter Head
Accurate citrus disease identification is essential for targeted orchard pesticide application. Current models struggle with accuracy and efficiency due to diverse leaf lesion patterns and complex orchard environments. This study presents YOLOv8n-DE, an improved lightweight YOLOv8-based model for en...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/1971 |
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| author | Xu Guo Xingmeng Wang Wenhao Zhu Simon X. Yang Lepeng Song Ping Li Qinzheng Li |
| author_facet | Xu Guo Xingmeng Wang Wenhao Zhu Simon X. Yang Lepeng Song Ping Li Qinzheng Li |
| author_sort | Xu Guo |
| collection | DOAJ |
| description | Accurate citrus disease identification is essential for targeted orchard pesticide application. Current models struggle with accuracy and efficiency due to diverse leaf lesion patterns and complex orchard environments. This study presents YOLOv8n-DE, an improved lightweight YOLOv8-based model for enhanced citrus disease detection. It introduces the DR module structure for effective feature enhancement and the Detect_Shared architecture for parameter efficiency. Evaluated on public and orchard-collected datasets, YOLOv8n-DE achieves 97.6% classification accuracy, 91.8% recall, and 97.3% mAP, with a 90.4% mAP for challenging diseases. Compared to the original YOLOv8, it reduces parameters by 48.17%, computational load by 59.26%, and model size by 41.94%, while significantly decreasing classification and regression errors, and false positives/negatives. YOLOv8n-DE offers outstanding performance and lightweight advantages for citrus disease detection, supporting precision agriculture development in orchards. |
| format | Article |
| id | doaj-art-422470bed9df4deb8db9df24f0331388 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-422470bed9df4deb8db9df24f03313882025-08-20T03:08:56ZengMDPI AGSensors1424-82202025-03-01257197110.3390/s25071971Citrus Disease Detection Based on Dilated Reparam Feature Enhancement and Shared Parameter HeadXu Guo0Xingmeng Wang1Wenhao Zhu2Simon X. Yang3Lepeng Song4Ping Li5Qinzheng Li6School of Big Data and Automation, Chongqing Chemical Industry Vocational College, Chongqing 401228, ChinaSchool of Electronic and Electrical Engineering, Chongqing University of Science & Technology, Chongqing 401331, ChinaSchool of Electronic and Electrical Engineering, Chongqing University of Science & Technology, Chongqing 401331, ChinaAdvanced Robotics and Intelligent Systems Laboratory, School of Engineering, University of Guelph, Guelph, ON N1G 2W1, CanadaSchool of Electronic and Electrical Engineering, Chongqing University of Science & Technology, Chongqing 401331, ChinaChongqing Academy of Agricultural Sciences, Chongqing 400039, ChinaSchool of Electronic and Electrical Engineering, Chongqing University of Science & Technology, Chongqing 401331, ChinaAccurate citrus disease identification is essential for targeted orchard pesticide application. Current models struggle with accuracy and efficiency due to diverse leaf lesion patterns and complex orchard environments. This study presents YOLOv8n-DE, an improved lightweight YOLOv8-based model for enhanced citrus disease detection. It introduces the DR module structure for effective feature enhancement and the Detect_Shared architecture for parameter efficiency. Evaluated on public and orchard-collected datasets, YOLOv8n-DE achieves 97.6% classification accuracy, 91.8% recall, and 97.3% mAP, with a 90.4% mAP for challenging diseases. Compared to the original YOLOv8, it reduces parameters by 48.17%, computational load by 59.26%, and model size by 41.94%, while significantly decreasing classification and regression errors, and false positives/negatives. YOLOv8n-DE offers outstanding performance and lightweight advantages for citrus disease detection, supporting precision agriculture development in orchards.https://www.mdpi.com/1424-8220/25/7/1971YOLOv8n-DEdilated reparam feature enhancementshared parameter headcitrus disease detectionreal-time detection |
| spellingShingle | Xu Guo Xingmeng Wang Wenhao Zhu Simon X. Yang Lepeng Song Ping Li Qinzheng Li Citrus Disease Detection Based on Dilated Reparam Feature Enhancement and Shared Parameter Head Sensors YOLOv8n-DE dilated reparam feature enhancement shared parameter head citrus disease detection real-time detection |
| title | Citrus Disease Detection Based on Dilated Reparam Feature Enhancement and Shared Parameter Head |
| title_full | Citrus Disease Detection Based on Dilated Reparam Feature Enhancement and Shared Parameter Head |
| title_fullStr | Citrus Disease Detection Based on Dilated Reparam Feature Enhancement and Shared Parameter Head |
| title_full_unstemmed | Citrus Disease Detection Based on Dilated Reparam Feature Enhancement and Shared Parameter Head |
| title_short | Citrus Disease Detection Based on Dilated Reparam Feature Enhancement and Shared Parameter Head |
| title_sort | citrus disease detection based on dilated reparam feature enhancement and shared parameter head |
| topic | YOLOv8n-DE dilated reparam feature enhancement shared parameter head citrus disease detection real-time detection |
| url | https://www.mdpi.com/1424-8220/25/7/1971 |
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