GSD-YOLO: A Lightweight Decoupled Wheat Scab Spore Detection Network Based on Yolov7-Tiny
Aimed at the problem of the difference between intra-class and inter-class pathogenic spores of Wheat Scab image being small and difficult to distinguish, in this paper, we propose a lightweight decoupled Wheat Scab spore detection network based on Yolov7-tiny (GSD-YOLO). Specifically, considering t...
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MDPI AG
2024-12-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/14/12/2278 |
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| author | Dongyan Zhang Wenfeng Tao Tao Cheng Xingen Zhou Gensheng Hu Hongbo Qiao Wei Guo Ziheng Wang Chunyan Gu |
| author_facet | Dongyan Zhang Wenfeng Tao Tao Cheng Xingen Zhou Gensheng Hu Hongbo Qiao Wei Guo Ziheng Wang Chunyan Gu |
| author_sort | Dongyan Zhang |
| collection | DOAJ |
| description | Aimed at the problem of the difference between intra-class and inter-class pathogenic spores of Wheat Scab image being small and difficult to distinguish, in this paper, we propose a lightweight decoupled Wheat Scab spore detection network based on Yolov7-tiny (GSD-YOLO). Specifically, considering the limitations of the storage space and power consumption of actual field detection equipment, the original detection head is optimized as a decoupled head, and the GSConv lightweight module is embedded to reduce the parameters of the model and the number of calculations required. In addition, we utilize an improved Spore–Copy data augmentation strategy to improve the detection performance and generalization ability of the algorithm to fit the large numbers, morphology, and variety of wheat disease spores in the actual field and to improve the efficiency of constructing a large data set of diverse spores. The experimental results show that the mAP of the proposed algorithm reaches 98.0%, which is 3.9 percentage points higher than that of the original model. At the same time, the detection speed of the algorithm is 114 f/s, and the memory is 13.1 MB, which meets the application requirements of hardware deployment and real-time detection. It can provide some technical support to the prevention and grading of Wheat Scab in actual farmland. |
| format | Article |
| id | doaj-art-a6c2c9e9246040a6b181f95b8f722ed8 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-a6c2c9e9246040a6b181f95b8f722ed82024-12-27T14:03:14ZengMDPI AGAgriculture2077-04722024-12-011412227810.3390/agriculture14122278GSD-YOLO: A Lightweight Decoupled Wheat Scab Spore Detection Network Based on Yolov7-TinyDongyan Zhang0Wenfeng Tao1Tao Cheng2Xingen Zhou3Gensheng Hu4Hongbo Qiao5Wei Guo6Ziheng Wang7Chunyan Gu8College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, ChinaNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, ChinaTexas A&M AgriLife Research Center, 1509 Aggie Drive, Beaumont, TX 77713, USANational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, ChinaCollege of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, ChinaCollege of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, ChinaInstitute of Plant Protection and Agro-Products Safety, Anhui Academy of Agricultural Sciences, Hefei 230031, ChinaAimed at the problem of the difference between intra-class and inter-class pathogenic spores of Wheat Scab image being small and difficult to distinguish, in this paper, we propose a lightweight decoupled Wheat Scab spore detection network based on Yolov7-tiny (GSD-YOLO). Specifically, considering the limitations of the storage space and power consumption of actual field detection equipment, the original detection head is optimized as a decoupled head, and the GSConv lightweight module is embedded to reduce the parameters of the model and the number of calculations required. In addition, we utilize an improved Spore–Copy data augmentation strategy to improve the detection performance and generalization ability of the algorithm to fit the large numbers, morphology, and variety of wheat disease spores in the actual field and to improve the efficiency of constructing a large data set of diverse spores. The experimental results show that the mAP of the proposed algorithm reaches 98.0%, which is 3.9 percentage points higher than that of the original model. At the same time, the detection speed of the algorithm is 114 f/s, and the memory is 13.1 MB, which meets the application requirements of hardware deployment and real-time detection. It can provide some technical support to the prevention and grading of Wheat Scab in actual farmland.https://www.mdpi.com/2077-0472/14/12/2278wheat scab sporestarget detectionYOLOGSConvdecoupled head |
| spellingShingle | Dongyan Zhang Wenfeng Tao Tao Cheng Xingen Zhou Gensheng Hu Hongbo Qiao Wei Guo Ziheng Wang Chunyan Gu GSD-YOLO: A Lightweight Decoupled Wheat Scab Spore Detection Network Based on Yolov7-Tiny Agriculture wheat scab spores target detection YOLO GSConv decoupled head |
| title | GSD-YOLO: A Lightweight Decoupled Wheat Scab Spore Detection Network Based on Yolov7-Tiny |
| title_full | GSD-YOLO: A Lightweight Decoupled Wheat Scab Spore Detection Network Based on Yolov7-Tiny |
| title_fullStr | GSD-YOLO: A Lightweight Decoupled Wheat Scab Spore Detection Network Based on Yolov7-Tiny |
| title_full_unstemmed | GSD-YOLO: A Lightweight Decoupled Wheat Scab Spore Detection Network Based on Yolov7-Tiny |
| title_short | GSD-YOLO: A Lightweight Decoupled Wheat Scab Spore Detection Network Based on Yolov7-Tiny |
| title_sort | gsd yolo a lightweight decoupled wheat scab spore detection network based on yolov7 tiny |
| topic | wheat scab spores target detection YOLO GSConv decoupled head |
| url | https://www.mdpi.com/2077-0472/14/12/2278 |
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