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

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

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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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Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
CBAM: Convolutional Block Attention Module
Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_1 · Cited by 25,853
Non-local Neural Networks
Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596
A Review of Yolo Algorithm Developments
Jiang, P., Ergu, D., Liu, F., Cai, Y., & Ma, B. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.01.135 · Cited by 2,783 · cited 1×
“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
Liu, G., Christian, N. J., Mbouembe, P. L. T., & Kim, J. H. (2020) · Sensors · doi:10.3390/s20072145 · Cited by 441
A detection algorithm for cherry fruits based on the improved YOLO-v4 model
Gai, R., Chen, N., & Yuan, H. (2023) · Neural Computing and Applications · doi:10.1007/s00521-021-06029-z · Cited by 388
Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique
Gulzar, Y. (2023) · Sustainability · doi:10.3390/su15031906 · Cited by 375
Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network
Hu, B., & Wang, J. (2020) · IEEE Access · doi:10.1109/access.2020.3001349 · Cited by 364
Self-citation
Tomato Fruit Detection and Counting in Greenhouses Using Deep Learning
Afonso, M. V., Fonteijn, H., Fiorentin, F. S., Lensink, D., Mooij, M., Faber, N., Polder, G., & Wehrens, R. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.571299 · Cited by 261
Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse
Magalhães, S. A., de Castro, L. M. G., Moreira, G., dos Santos, F. N., Cunha, M., Dias, J. M. M., & Moreira, A. P. (2021) · Sensors · doi:10.3390/s21103569 · Cited by 198

Show all 29 references

Research works printed but not linked: 3

Ünal, Z., and Kızıldeniz, T. (2023). Potato Production Worldwide, Academic Press.
Zhu, 2020, Automatic Identification Technology of Lycium barbarum Flowering Period and Fruit Ripening Period Based on Faster R-CNN, Chin. J. Agrometeorol., 41, 668
Peng, 2018, General improved SSD model for picking object recognition of multiple fruits in natural environment, Trans. Chin. Soc. Agric. Eng., 34, 155

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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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