Cited by 324Smart Agriculture and AIAdvanced Neural Network ApplicationsIndustrial Vision Systems and Defect Detection

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A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention

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Yang, Guoliang, Wang, Jixiang, Nie, Ziling, Yang, Hao, Yu, Shuaiying·2023·Agronomy

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Abstract

A tomato automatic detection method based on an improved YOLOv8s model is proposed to address the low automation level in tomato harvesting in agriculture. The proposed method provides technical support for the automatic harvesting and classification of tomatoes in agricultural production activities. The proposed method has three key components. Firstly, the depthwise separable convolution (DSConv) technique replaces the ordinary convolution, which reduces the computational complexity by generating a large number of feature maps with a small amount of calculation. Secondly, the dual-path attention gate module (DPAG) is designed to improve the model’s detection precision in complex environments by enhancing the network’s ability to distinguish between tomatoes and the background. Thirdly, the feature enhancement module (FEM) is added to highlight the target details, prevent the loss of effective features, and improve detection precision. We built, trained, and tested the tomato dataset, which included 3098 images and 3 classes. The proposed algorithm’s performance was evaluated by comparison with the SSD, faster R-CNN, YOLOv4, YOLOv5, and YOLOv7 algorithms. Precision, recall rate, and mAP (mean average precision) were used for evaluation. The test results show that the improved YOLOv8s network has a lower loss and 93.4% mAP on this dataset. This improvement is a 1.5% increase compared to before the improvement. The precision increased by 2%, and the recall rate increased by 0.8%. Moreover, the proposed algorithm significantly reduced the model size from 22 M to 16 M, while achieving a detection speed of 138.8 FPS, which satisfies the real-time detection requirement. The proposed method strikes a balance between model size and detection precision, enabling it to meet agriculture’s tomato detection requirements. The research model in this paper will provide technical support for a tomato picking robot to ensure the fast and accurate operation of the picking robot.

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Authors: Yang, G., Wang, J., Nie, Z., Yang, H., & Yu, S.
Affiliations: Jiangxi University of Science and Technology (CN)
Corresponding author: Yang, Guoliang
ORCID: Yang, Guoliang
Wang, Jixiang
Yang, Hao
Yu, Shuaiying
Venue: Agronomy
Publisher: MDPI AG
Published: 2023-07-09
Volume: 13
Issue: 7
Pages: 1824
ISSN: 2073-4395 (ISSN-L)
DOI: 10.3390/agronomy13071824
OpenAlex: W4383695095
Cited by: 323
References: 29
Citations by year: 2026: 49 · 2025: 119 · 2024: 145 · 2023: 10
FWCI: 50.98
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Agronomy (publishedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Science and Technology Project of the Education Department of Jiangxi Province (GJJ180484, GJJ190450)
Science and Technology Program of Jiangxi Provincial Education Department (GJJ180484, GJJ190450)
Topics: Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
Keywords: tomato detection, YOLOv8, depthwise separable convolution, feature enhancement module, lightweight object detection, real-time detection, agricultural robotics

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A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention

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