GSD-YOLO: A Lightweight Decoupled Wheat Scab Spore Detection Network Based on Yolov7-Tiny

Dongyan Zhang, Wenfeng Tao, Tao Cheng, Xingen Zhou (), Gensheng Hu, Hongbo Qiao, Wei Guo, Ziheng Wang and Chunyan Gu ()
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Dongyan Zhang: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Wenfeng Tao: National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
Tao Cheng: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Xingen Zhou: Texas A&M AgriLife Research Center, 1509 Aggie Drive, Beaumont, TX 77713, USA
Gensheng Hu: National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
Hongbo Qiao: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Wei Guo: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Ziheng Wang: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Chunyan Gu: Institute of Plant Protection and Agro-Products Safety, Anhui Academy of Agricultural Sciences, Hefei 230031, China

Agriculture, 2024, vol. 14, issue 12, 1-10

Abstract: 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.

Keywords: wheat scab spores; target detection; YOLO; GSConv; decoupled head (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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