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Open AccessArticle
Identification of Insect Pests on Soybean Leaves Based on SP-YOLO
by
Kebei Qin
Kebei Qin ,
Jie Zhang
Jie Zhang * and
Yue Hu
Yue Hu
College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(7), 1586; https://doi.org/10.3390/agronomy14071586 (registering DOI)
Submission received: 3 July 2024
/
Revised: 19 July 2024
/
Accepted: 19 July 2024
/
Published: 20 July 2024
Abstract
Soybean insect pests can seriously affect soybean yield, so efficient and accurate detection of soybean insect pests is crucial for soybean production. However, pest detection in complex environments suffers from the problems of small pest targets, large inter-class feature similarity, and background interference with feature extraction. To address the above problems, this study proposes the detection algorithm SP-YOLO for soybean pests based on YOLOv8n. The model utilizes FasterNet to replace the backbone of YOLOv8n, which reduces redundant features and improves the model’s ability to extract effective features. Second, we propose the PConvGLU architecture, which enhances the capture and representation of image details while reducing computation and memory requirements. In addition, this study proposes a lightweight shared detection header, which enables the model parameter amount computation to be reduced and the model accuracy to be further improved by shared convolution and GroupNorm. The improved model achieves 80.8% precision, 66.4% recall, and 73% average precision, which is 6%, 5.4%, and 5.2%, respectively, compared to YOLOv8n. The FPS reaches 256.4, and the final model size is only 6.2 M, while the number of computational quantities of covariates is basically comparable to that of the original model. The detection capability of SP-YOLO is significantly enhanced compared to that of the existing methods, which provides a good solution for soybean pest detection. SP-YOLO provides an effective technical support for soybean pest detection.
Share and Cite
MDPI and ACS Style
Qin, K.; Zhang, J.; Hu, Y.
Identification of Insect Pests on Soybean Leaves Based on SP-YOLO. Agronomy 2024, 14, 1586.
https://doi.org/10.3390/agronomy14071586
AMA Style
Qin K, Zhang J, Hu Y.
Identification of Insect Pests on Soybean Leaves Based on SP-YOLO. Agronomy. 2024; 14(7):1586.
https://doi.org/10.3390/agronomy14071586
Chicago/Turabian Style
Qin, Kebei, Jie Zhang, and Yue Hu.
2024. "Identification of Insect Pests on Soybean Leaves Based on SP-YOLO" Agronomy 14, no. 7: 1586.
https://doi.org/10.3390/agronomy14071586
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