YOLO performance analysis for real-time detection of soybean pests

Tetila, Everton Castelão; da Silveira, Fábio Amaral Godoy; da Costa, Anderson Bessa; Amorim, Willian Paraguassu; Astolfi, Gilberto; Pistori, Hemerson; Barbedo, Jayme Garcia Arnal

doi:10.1016/j.atech.2024.100405

Smart Agricultural Technology

2024

DOI: 10.1016/j.atech.2024.100405

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YOLO performance analysis for real-time detection of soybean pests

Everton Castelão Tetila,

Fábio Amaral Godoy da Silveira,

Anderson Bessa da Costa

et al.

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Cited by 2 publications

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Innovations in seafood freshness quality: Non-destructive detection of freshness in Litopenaeus vannamei using the YOLO-shrimp model

Hou,

Zhong,

Zheng

et al. 2025

Food Chemistry

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Innovations in seafood freshness quality: Non-destructive detection of freshness in Litopenaeus vannamei using the YOLO-shrimp model

Hou,

Zhong,

Zheng

et al. 2025

Food Chemistry

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Identification of Insect Pests on Soybean Leaves Based on SP-YOLO

Qin,

Zhang,

2024

Agronomy

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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.

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YOLO performance analysis for real-time detection of soybean pests

Cited by 2 publications

References 18 publications

Innovations in seafood freshness quality: Non-destructive detection of freshness in Litopenaeus vannamei using the YOLO-shrimp model

Innovations in seafood freshness quality: Non-destructive detection of freshness in Litopenaeus vannamei using the YOLO-shrimp model

Identification of Insect Pests on Soybean Leaves Based on SP-YOLO

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