The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention
Open accessAbstract
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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“| YOLOv8s [100] | mAP | Lightweight YOLOv8s model with mAP of 93.4%, suitable for small devices. | Tomato image data |”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [100]
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Cites
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“On the other hand, one-stage object detection algorithms, which include SSD [20] and YOLO [21], have faster recognition speed while maintaining the same precision as the two-stage object detection algorithms, thereby meeting real-time requirements.”
YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3
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A detection algorithm for cherry fruits based on the improved YOLO-v4 model
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Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique
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Cited by
Sorted by citations
Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383
Sapkota, R., Flores-Calero, M., Qureshi, R., Badjugar, C., Nepal, U., Poulose, A., Zeno, P. J., Vaddevolu, U. B. P., Khan, S., Shoman, M., et al. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11253-3 · Cited by 528
Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments
Sapkota, R., Ahmed, D., & Karkee, M. (2024) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2024.07.001 · Cited by 272
Solimani, F., Cardellicchio, A., Dimauro, G., Petrozza, A., Summerer, S., Cellini, F., & Renò, V. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.108728 · Cited by 122
ALF-YOLO: Enhanced YOLOv8 based on multiscale attention feature fusion for ship detection
Wang, S., Li, Y., & Qiao, S. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.118233 · Cited by 108
Metadata
- Authors
- Yang, G., Wang, J., Nie, Z., Yang, H., & Yu, S.
- Affiliations
- Jiangxi University of Science and Technology (CN)
- Corresponding author
- Yang, Guoliang
- Venue
- Agronomy
- Publisher
- MDPI AG
- Published
- 2023-07-09
- Volume
- 13
- Issue
- 7
- Pages
- 1824
- ISSN
- 2073-4395 (ISSN-L)
- 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
- 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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