A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention

Yang, Guoliang; Wang, Jixiang; Nie, Ziling; Yang, Hao; Yu, Shuaiying

doi:10.3390/agronomy13071824

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

A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention

Open access

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

DOI 10.3390/agronomy13071824

Open this paper in CoChat — free account Open PDF

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.

Integrity signals

Registry

Crossref · 10.3390/agronomy13071824

Cites

29 of 29 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Lightweight YOLOv8s model achieved mAP of 93.4%, suitable for small devices on tomato image data.
“| 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]

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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 428 · see citing works
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 · see citing works
Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique
Gulzar, Y. (2023) · Sustainability · doi:10.3390/su15031906 · Cited by 375 · see citing works
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 344 · see citing works
Self-citation
Tomato Fruit Detection and Counting in Greenhouses Using Deep Learning
Afonso, M. V., Fonteijn, H. M. J., 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 245 · see citing works
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 193 · see citing works
Deep learning-based apple detection using a suppression mask R-CNN
Chu, P., Li, Z., Lammers, K., Lu, R., & Liu, X. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.04.022 · Cited by 160 · see citing works
ASCU-Net: Attention Gate, Spatial and Channel Attention U-Net for Skin Lesion Segmentation
Tong, X., Wei, J., Bei, S., Su, S., Zuo, Z. C., & Wu, P. (2021) · Diagnostics · doi:10.3390/diagnostics11030501 · Cited by 158 · see citing works
Fruit detection in natural environment using partial shape matching and probabilistic Hough transform
Lin, G., Tang, Y., Zou, X., Cheng, J., & Xiong, J. (2019) · Precision Agriculture · doi:10.1007/s11119-019-09662-w · Cited by 151 · see citing works
Enhancing Image Annotation Technique of Fruit Classification Using a Deep Learning Approach
Mamat, N., Othman, M. F., Abdulghafor, R. A. A., Alwan, A. A., & Gulzar, Y. (2023) · Sustainability · doi:10.3390/su15020901 · Cited by 139 · see citing works
Intact Detection of Highly Occluded Immature Tomatoes on Plants Using Deep Learning Techniques
Mu, Y., Chen, T.-S., Ninomiya, S., & Guo, W. (2020) · Sensors · doi:10.3390/s20102984 · Cited by 123 · see citing works
Mature Tomato Fruit Detection Algorithm Based on improved HSV and Watershed Algorithm
Malik, M. H., Zhang, T., Li, H., Zhang, M., Shabbir, S., & Saeed, A. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.08.183 · Cited by 110 · see citing works
Benchmark of Deep Learning and a Proposed HSV Colour Space Models for the Detection and Classification of Greenhouse Tomato
Moreira, G., Magalhães, S. A., Pinho, T. M., dos Santos, F. N., & Cunha, M. (2022) · Agronomy · doi:10.3390/agronomy12020356 · Cited by 91 · see citing works
Tomatoes
Heuvelink, E. (2018) · CABI eBooks · doi:10.1079/9781780641935.0000 · Cited by 82 · see citing works
Context-Aware RCNN: A Baseline for Action Detection in Videos
Wu, J.-C., Kuang, Z., Wang, L., Zhang, W., & Wu, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58595-2_27 · Cited by 80 · see citing works
Clustered tomato detection and picking point location using machine learning-aided image analysis for automatic robotic harvesting
Bai, Y., Mao, S., Zhou, J. C., & Zhang, B. (2022) · Precision Agriculture · doi:10.1007/s11119-022-09972-6 · Cited by 80 · see citing works
Automatic Tomato and Peduncle Location System Based on Computer Vision for Use in Robotized Harvesting
Benavides, M., Garbín, M. C., Sánchez-Molina, J. A., & Rodríguez, F. (2020) · Applied Sciences · doi:10.3390/app10175887 · Cited by 65 · see citing works
Tomato Maturity Recognition Model Based on Improved YOLOv5 in Greenhouse
Li, R., Ji, Z., Hu, S., Huang, X., Yang, J., & Li, W. (2023) · Agronomy · doi:10.3390/agronomy13020603 · Cited by 60 · see citing works
Tomato Young Fruits Detection Method under Near Color Background Based on Improved Faster R-CNN with Attention Mechanism
Wang, P., Niu, T., & He, D. (2021) · Agriculture · doi:10.3390/agriculture11111059 · Cited by 46 · see citing works
RDE-YOLOv7: An Improved Model Based on YOLOv7 for Better Performance in Detecting Dragon Fruits
Zhou, J., Zhang, Y., & Wang, J. (2023) · Agronomy · doi:10.3390/agronomy13041042 · Cited by 41 · see citing works
Self-citation
A method for organs classification and fruit counting on pomegranate trees based on multi-features fusion and support vector machine by 3D point cloud
Zhang, C., Zhang, K., Ge, L., Zou, K., Wang, S., Zhang, J., & Li, W. (2020) · Scientia Horticulturae · doi:10.1016/j.scienta.2020.109791 · Cited by 39 · see citing works
Post-harvest practices and loss assessment in tomato (Solanum lycopersicum L.) in Kathmandu, Nepal
Tiwari, I., Shah, K. K., Tripathi, S., Modi, B., Shrestha, J., Pandey, H. P., Bhattarai, B. P., & Rajbhandari, B. P. (2020) · Journal of Agriculture and Natural Resources · doi:10.3126/janr.v3i2.32545 · Cited by 30 · see citing works
Application of a Fast RCNN Based on Upper and Lower Layers in Face Recognition
Jiang, L., Jia, C., Todo, H., Tang, Z., Liu, S., & Li, Y. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/9945934 · Cited by 19 · see citing works
A detection method of Edge Coherent Mode based on improved SSD
Zeng, F., Liu, Y., Ye, Y., Zhou, J., & Liu, X. (2022) · Fusion Engineering and Design · doi:10.1016/j.fusengdes.2022.113141 · Cited by 19 · see citing works
Cotton Stubble Detection Based on Improved YOLOv3
Yang, Y., Li, J., Nie, J., Yang, S., & Tang, J. (2023) · Agronomy · doi:10.3390/agronomy13051271 · Cited by 12 · see citing works

Flagged first, then by citations

Sorted by citations

The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection
Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383 · see citing works
YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series
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 454 · see citing works
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 208 · see citing works
Optimizing tomato plant phenotyping detection: Boosting YOLOv8 architecture to tackle data complexity
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 107 · see citing works
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 102 · see citing works
BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection
Kang, M., Ting, C., Ting, F. F., & Phan, R. C. ‐. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72111-3_4 · Cited by 82 · see citing works
Detection of Pine Wilt Disease Using AAV Remote Sensing With an Improved YOLO Model
Wang, L., Cai, J., Wang, T., Zhao, J., Gadekallu, T. R., & Fang, K. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3478333 · Cited by 78 · see citing works
Strawberry Detection and Ripeness Classification Using YOLOv8+ Model and Image Processing Method
Wang, C., Wang, H., Han, Q., Zhang, Z., Kong, D., & Zou, X. (2024) · Agriculture · doi:10.3390/agriculture14050751 · Cited by 62 · see citing works
STRAW-YOLO: A detection method for strawberry fruits targets and key points
Ma, Z., Dong, N., Gu, J., Cheng, H., Meng, Z., & Du, X. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109853 · Cited by 64 · see citing works
PG-YOLO: An efficient detection algorithm for pomegranate before fruit thinning
Wang, J., Liu, M., Du, Y., Zhao, M., Jia, H., Guo, Z., Su, Y., Lu, D., & Liu, Y. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108700 · Cited by 60 · see citing works
Self-citation
A novel lightweight real-time traffic sign detection method based on an embedded device and YOLOv8
Luo, Y., Ci, Y., Jiang, S., & Wei, X. (2024) · Journal of Real-Time Image Processing · doi:10.1007/s11554-023-01403-7 · Cited by 59 · see citing works
Synthetic meets authentic: Leveraging LLM generated datasets for YOLO11 and YOLOv10-based apple detection through machine vision sensors
Sapkota, R., Meng, Z., & Karkee, M. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100614 · Cited by 58 · see citing works
Deep Learning for Tomato Disease Detection with YOLOv8
Zayani, H. M., Ammar, I., Ghodhbani, R., Maqbool, A., Saidani, T. S., Slimane, J. B., Kachoukh, A., Kouki, M., Kallel, M., Alsuwaylimi, A. A., et al. (2024) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.7064 · Cited by 51 · see citing works
HP-YOLOv8: High-Precision Small Object Detection Algorithm for Remote Sensing Images
Yao, G., Zhu, S., Zhang, L., & Qi, M. (2024) · Sensors · doi:10.3390/s24154858 · Cited by 47 · see citing works
Enhancing rail safety through real-time defect detection: A novel lightweight network approach
Cao, Y., Liu, Y., Sun, Y., Su, S., & Wang, F. (2024) · Accident Analysis & Prevention · doi:10.1016/j.aap.2024.107617 · Cited by 45 · see citing works
YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once SeriesPreprint
Sapkota, R., Qureshi, R., Flores-Calero, M. J., Hussain, M., Badjugar, C., Nepal, U., Poulose, A., Zeno, P. J., Vaddevolu, U. B. P., Yan, H., et al. (2024) · Preprints.org · doi:10.20944/preprints202406.1366.v1 · Cited by 45 · see citing works
A corn canopy organs detection method based on improved DBi-YOLOv8 network
Guan, H., Deng, H., Ma, X., Zhang, T., Zhang, Y., Zhu, T., Zhou, H., Gu, Z., & Lu, Y. (2024) · European Journal of Agronomy · doi:10.1016/j.eja.2023.127076 · Cited by 41 · see citing works
Real-time Object Detection in Autonomous Vehicles with YOLO
Alahdal, N. M., Abukhodair, F., Meftah, L. H., & Cherif, A. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.09.392 · Cited by 41 · see citing works
Advanced aerial monitoring and vehicle classification for intelligent transportation systems with YOLOv8 variants
Bakırcı, M. (2025) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2025.104134 · Cited by 37 · see citing works
Wood defect detection based on the CWB-YOLOv8 algorithm
An, H., Liang, Z., Qin, M., Huang, Y., Xiong, F., & Zeng, G. (2024) · Journal of Wood Science · doi:10.1186/s10086-024-02139-z · Cited by 33 · see citing works
A real-time human bone fracture detection and classification from multi-modal images using deep learning technique
Parvin, S., & Rahman, A. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05588-7 · Cited by 33 · see citing works
Advancing Precision Agriculture: Enhanced Weed Detection Using the Optimized YOLOv8T Model
Sharma, S., & Vardhan, M. (2024) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-024-09419-2 · Cited by 33 · see citing works
An Improved Fire and Smoke Detection Method Based on YOLOv8n for Smart Factories
Zhang, Z., Tan, L., & Tiong, R. L. K. (2024) · Sensors · doi:10.3390/s24154786 · Cited by 32 · see citing works
Application of YOLOv8 and Detectron2 for Bullet Hole Detection and Score Calculation from Shooting Cards
Butt, M., Glas, N., Monsuur, J., Stoop, R., & de Keijzer, A. (2023) · AI · doi:10.3390/ai5010005 · Cited by 32 · see citing works
A Lightweight Model of Underwater Object Detection Based on YOLOv8n for an Edge Computing Platform
Fan, Y., Zhang, L., & Li, P. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12050697 · Cited by 31 · see citing works
A Deep-Learning-Based Model for the Detection of Diseased Tomato Leaves
Abdullah, A., Amran, G. A., Tahmid, S. M. A., Alabrah, A. A., AL-Bakhrani, A. A., & Ali, A. (2024) · Agronomy · doi:10.3390/agronomy14071593 · Cited by 31 · see citing works
Chili Pepper Object Detection Method Based on Improved YOLOv8n
Ma, N., Wu, Y., Bo, Y., & Yan, H. (2024) · Plants · doi:10.3390/plants13172402 · Cited by 31 · see citing works
Ripe Tomato Detection Algorithm Based on Improved YOLOv9
Wang, Y., Rong, Q., & Hu, C. (2024) · Plants · doi:10.3390/plants13223253 · Cited by 33 · see citing works
RSR-YOLO: a real-time method for small target tomato detection based on improved YOLOv8 network
Yue, X., Qi, K., Yang, F., Na, X., Liu, Y., & Liu, C. (2024) · Discover Applied Sciences · doi:10.1007/s42452-024-05914-1 · Cited by 31 · see citing works
Transmission line insulator defect detection algorithm based on MAP-YOLOv8
Xu, Z.-Y., & Tang, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-92445-3 · Cited by 30 · see citing works
Revolutionizing Agriculture: Real-Time Ripe Tomato Detection With the Enhanced Tomato-YOLOv7 System
Guo, J., Yang, Y., Lin, X., Memon, M. S., Liu, W., Zhang, M., & Sun, E. (2023) · IEEE Access · doi:10.1109/access.2023.3336562 · Cited by 29 · see citing works
YOLOv8-PoseBoost: Advancements in Multimodal Robot Pose Keypoint Detection
Wang, F., Wang, G., & Lu, B. (2024) · Electronics · doi:10.3390/electronics13061046 · Cited by 29 · see citing works
Real-time detection of mature table grapes using ESP-YOLO network on embedded platforms
Chen, J., Chen, H., Xu, F., Lin, M., Zhang, D., & Zhang, L. (2024) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2024.07.014 · Cited by 29 · see citing works
A lightweight model based on you only look once for pomegranate before fruit thinning in complex environment
Du, Y., Han, Y., Su, Y., & Wang, J. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109123 · Cited by 29 · see citing works
Self-citation
Y-HRNet: Research on multi-category cherry tomato instance segmentation model based on improved YOLOv7 and HRNet fusion
Liu, M., Chen, W., Cheng, J., Wang, Y., & Zhao, C. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109531 · Cited by 28 · see citing works
A lightweight YOLOv8 based on attention mechanism for mango pest and disease detection
Wang, J., & Wang, J. (2024) · Journal of Real-Time Image Processing · doi:10.1007/s11554-024-01505-w · Cited by 27 · see citing works
Self-citation
Tomato Recognition Method Based on the YOLOv8-Tomato Model in Complex Greenhouse Environments
Zheng, S., Jia, X., He, M., Zheng, Z., Lin, T., & Weng, W. (2024) · Agronomy · doi:10.3390/agronomy14081764 · Cited by 27 · see citing works
GLU-YOLOv8: An Improved Pest and Disease Target Detection Algorithm Based on YOLOv8
Yue, G., Liu, Y., Niu, T., Liu, L., An, L., Wang, Z., & Duan, M. (2024) · Forests · doi:10.3390/f15091486 · Cited by 26 · see citing works
Citrus Tree Canopy Segmentation of Orchard Spraying Robot Based on RGB-D Image and the Improved DeepLabv3+
Xue, X., Luo, Q., Bu, M., Li, Z., Lyu, S., & Song, S. (2023) · Agronomy · doi:10.3390/agronomy13082059 · Cited by 26 · see citing works
Improved Tomato Disease Detection with YOLOv5 and YOLOv8
Ahmed, R., & Abd-Elkawy, E. H. (2024) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.7262 · Cited by 26 · see citing works
Determination of tomato leafminer: Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) damage on tomato using deep learning instance segmentation method
Uygun, T., & Özgüven, M. M. (2024) · European Food Research and Technology · doi:10.1007/s00217-024-04516-w · Cited by 25 · see citing works
Tomato pose estimation using the association of tomato body and sepal
Jang, M., & Hwang, Y. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.108961 · Cited by 25 · see citing works
AgRegNet: A deep regression network for flower and fruit density estimation, localization, and counting in orchards
Bhattarai, U., Bhusal, S., Zhang, Q., & Karkee, M. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109534 · Cited by 25 · see citing works
RT-DETR-Tea: A Multi-Species Tea Bud Detection Model for Unstructured Environments
Chen, Y., Guo, Y., Li, J., Zhou, B., Chen, J.-M., Zhang, M., Cui, Y., & Tang, J. (2024) · Agriculture · doi:10.3390/agriculture14122256 · Cited by 23 · see citing works
Improved YOLOv8 for B-scan image flaw detection of the heavy-haul railway
Yu, C., Liu, Y., Cao, Y., Sun, Y., Su, S., Yang, W., & Wang, W. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad3a05 · Cited by 22 · see citing works
YOLO-Peach: A High-Performance Lightweight YOLOv8s-Based Model for Accurate Recognition and Enumeration of Peach Seedling Fruits
Shi, Y., Qing, S., Zhao, L. F., Wang, F., Yuwen, X., & Qu, M. (2024) · Agronomy · doi:10.3390/agronomy14081628 · Cited by 22 · see citing works
Intelligent Detection of Lightweight “Yuluxiang” Pear in Non-Structural Environment Based on YOLO-GEW
Ren, R., Sun, H., Zhang, S., Wang, N., Lu, X., Jing, J., Xin, M., & Cui, T. (2023) · Agronomy · doi:10.3390/agronomy13092418 · Cited by 21 · see citing works
Enhanced Lightweight YOLOX for Small Object Wildfire Detection in UAV Imagery
Luan, T., Zhou, S. K., Zhang, G., Song, Z., Wu, J., & Pan, W. (2024) · Sensors · doi:10.3390/s24092710 · Cited by 21 · see citing works
Improving real-time object detection in Internet-of-Things smart city traffic with YOLOv8-DSAF method
Li, Y., Huang, Y., & Tao, Q. (2024) · Scientific Reports · doi:10.1038/s41598-024-68115-1 · Cited by 29 · see citing works
A method for segmentation and localization of tomato lateral pruning points in complex environments based on improved YOLOV5
Liang, X., Wei, Z., & Chen, K. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109731 · Cited by 20 · see citing works
Research progress and prospect of key technologies of fruit target recognition for robotic fruit picking
Liu, S., Xue, J., Zhang, T., Lv, P., Qin, H., & Zhao, T. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1423338 · Cited by 20 · see citing works
Advancing capsicum detection in night-time greenhouse environments using deep learning models: Comparative analysis and improved zero-shot detection through fusion with a single-shot detector
Paul, A., & Machavaram, R. (2025) · Franklin Open · doi:10.1016/j.fraope.2025.100243 · Cited by 20 · see citing works
Real-time detection of plastic part surface defects using deep learning- based object detection model
Çelik, M. T., Arslankaya, S., & Yıldız, A. (2024) · Measurement · doi:10.1016/j.measurement.2024.114975 · Cited by 20 · see citing works
Pepper-YOLO: an lightweight model for green pepper detection and picking point localization in complex environments
Huang, Y., Zhong, Y., Zhong, D., Yang, C., Wei, L., Zou, Z., & Chen, R. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1508258 · Cited by 20 · see citing works
YOLOv8-EMSC: A lightweight fire recognition algorithm for large spaces
Li, D., Yang, T. P., Jin, Z., Wu, S., & Liu, Q. (2024) · Journal of Safety Science and Resilience · doi:10.1016/j.jnlssr.2024.06.003 · Cited by 19 · see citing works
A Method for Tomato Ripeness Recognition and Detection Based on an Improved YOLOv8 Model
Yang, Z., Li, Y., Han, Q., Wang, H., Li, C., & Wu, Z. (2024) · Horticulturae · doi:10.3390/horticulturae11010015 · Cited by 19 · see citing works
Study on the fusion of improved YOLOv8 and depth camera for bunch tomato stem picking point recognition and localization
Song, G., Wang, J., Ma, R., Shi, Y., & Wang, Y. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1447855 · Cited by 18 · see citing works
Self-citation
A novel lightweight model HGCA-YOLO: Application to recognition of invisible spears for white asparagus robotic harvesting
Zhang, P., Dai, N., Liu, X., Yuan, J., & Xin, Z. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.108852 · Cited by 20 · see citing works
RTFVE-YOLOv9: Real-time fruit volume estimation model integrating YOLOv9 and binocular stereo vision
Yi, W., Xia, S., Kuzmin, S. A., Герасимов, И. В., & Cheng, X. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110401 · Cited by 17 · see citing works
Performance Comparison of Cherry Tomato Ripeness Detection Using Multiple YOLO Models
Yang, D., & Ju, C. (2024) · AgriEngineering · doi:10.3390/agriengineering7010008 · Cited by 17 · see citing works
YOLOv8n-DDA-SAM: Accurate Cutting-Point Estimation for Robotic Cherry-Tomato Harvesting
Zhang, G., Cao, H., Jin, Y., Zhong, Y., Zhao, A., Zou, X., & Wang, H. (2024) · Agriculture · doi:10.3390/agriculture14071011 · Cited by 16 · see citing works
Enhanced YOLOv8 algorithm for leaf disease detection with lightweight GOCR-ELAN module and loss function: WSIoU
Wen, G., Li, M., Tan, Y., Shi, C., Luo, Y., & Luo, W. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109630 · Cited by 16 · see citing works
Detection Method for Rice Seedling Planting Conditions Based on Image Processing and an Improved YOLOv8n Model
Zhao, B., Zhang, Q. H., Liu, Y., Cui, Y., & Zhou, B. (2024) · Applied Sciences · doi:10.3390/app14062575 · Cited by 15 · see citing works
Diagnosis of Pressure Ulcer Stage Using On-Device AI
Chang, Y., Kim, J., Shin, H. W., Ha, C., Lee, S. Y., & Go, T. (2024) · Applied Sciences · doi:10.3390/app14167124 · Cited by 15 · see citing works
Enhancing fruit and vegetable detection in unconstrained environment with a novel dataset
Khanna, S., Chattopadhyay, C., & Kundu, S. (2024) · Scientia Horticulturae · doi:10.1016/j.scienta.2024.113580 · Cited by 15 · see citing works
Implementation of Smart Farm Systems Based on Fog Computing in Artificial Intelligence of Things Environments
Hong, S., Park, S., Youn, H., Lee, J.-Y., & Kwon, S. (2024) · Sensors · doi:10.3390/s24206689 · Cited by 15 · see citing works
Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques
Huong, P. T., Hien, L. T., Sơn, N. M., Tuan, H. C., & Nguyen, T. Q. (2025) · Journal of Algorithms & Computational Technology · doi:10.1177/17483026241309070 · Cited by 15 · see citing works
A Comparative Study of YOLO Series (v3–v10) with DeepSORT and StrongSORT: A Real-Time Tracking Performance Study
Alkandary, K., Yildiz, A. S., & Meng, H. (2025) · Electronics · doi:10.3390/electronics14050876 · Cited by 15 · see citing works
Detecting Plant Diseases Using Machine Learning Models
Kohut, N., Basystiuk, O., Shakhovska, N., & Melnykova, N. (2024) · Sustainability · doi:10.3390/su17010132 · Cited by 14 · see citing works
A Dynamic Kalman Filtering Method for Multi-Object Fruit Tracking and Counting in Complex Orchards
Zhai, Y., Zhang, L., Hu, X., Yang, F., & Huang, Y. (2025) · Sensors · doi:10.3390/s25134138 · Cited by 14 · see citing works
Optimizing object detection for autonomous robots: a comparative analysis of YOLO models
Vaghela, R., Vaishnani, D., Sarda, J. S., Thakkar, A., Nasit, Y., Brahma, B., & Bhoi, A. K. (2025) · Measurement · doi:10.1016/j.measurement.2025.118676 · Cited by 14 · see citing works
Hybrid Data Augmentation for Enhanced Crack Detection in Building Construction
Choi, S. J., Cha, H.-S., & Jiang, S. (2024) · Buildings · doi:10.3390/buildings14071929 · Cited by 14 · see citing works
Lightweight Tomato Leaf Intelligent Disease Detection Model Based on Adaptive Kernel Convolution and Feature Fusion
Ji, B., Li, H., Jin, X., Zhang, J., Tao, F., Li, P., Wang, J., & Fan, H. (2024) · IEEE Transactions on AgriFood Electronics · doi:10.1109/tafe.2024.3445119 · Cited by 15 · see citing works
Self-citation
DSC-YOLOv8n: An advanced automatic detection algorithm for urban flood levels
Wan, J., Shen, Y., Xue, F., Xu, Y., Qin, Y., Yang, T., Yang, G., & Wang, Q. J. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.132028 · Cited by 14 · see citing works
Self-citation
An Improved Forest Smoke Detection Model Based on YOLOv8
Wang, Y., Piao, Y., Wang, H., Zhang, H., & Li, B. (2024) · Forests · doi:10.3390/f15030409 · Cited by 13 · see citing works
High-Efficiency and High-Precision Ship Detection Algorithm Based on Improved YOLOv8n
Lan, K., Jiang, X., Ding, X., Lin, H., & Chan, S. (2024) · Mathematics · doi:10.3390/math12071072 · Cited by 13 · see citing works
The application prospects of robot pose estimation technology: exploring new directions based on YOLOv8-ApexNet
Tang, X., & Zhao, S. (2024) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2024.1374385 · Cited by 13 · see citing works
Detection and classification of breast cancer in mammographic images with fine-tuned convolutional neural networks
Luong, H. H., Nguyễn, H. T., & Thai-Nghe, N. (2024) · Journal of Information and Telecommunication · doi:10.1080/24751839.2024.2415033 · Cited by 14 · see citing works
The impact of backbone selection in YOLOv8 models on brain tumor localization
Ranjbarzadeh, R., Crane, M., & Bendechache, M. (2025) · Iran Journal of Computer Science · doi:10.1007/s42044-025-00258-4 · Cited by 13 · see citing works
An Improved YOLOv8 Model for Detecting Four Stages of Tomato Ripening and Its Application Deployment in a Greenhouse Environment
Sun, H., Zheng, Q., Yao, W., Wang, J. H., Liu, C., Yu, H., & Chen, C. (2025) · Agriculture · doi:10.3390/agriculture15090936 · Cited by 12 · see citing works
Self-citation
ML-DETR: Multiscale-Lite Detection Transformer for Identification of Mature Cherry Tomatoes
Tang, Z., Fang, L., Sun, S., Gong, Y., & Li, Q. '. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3608317 · Cited by 12 · see citing works
Panoptic Water Surface Visual Perception for USVs Using Monocular Camera Sensor
Xu, H., Zhang, X., He, J., Geng, Z., Yu, Y. F., & Cheng, Y. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3413088 · Cited by 12 · see citing works
YOLO-Chili: An Efficient Lightweight Network Model for Localization of Pepper Picking in Complex Environments
Chen, H., Zhang, R., Peng, J., Peng, H., Hu, W., Wang, Y., & Jiang, P. (2024) · Applied Sciences · doi:10.3390/app14135524 · Cited by 12 · see citing works
Detection Method for Inter-Turn Short Circuit Faults in Dry-Type Transformers Based on an Improved YOLOv8 Infrared Image Slicing-Aided Hyper-Inference Algorithm
Zhang, Z., Xia, J., Wen, Y.-C., Weng, L., Ma, Z., Yang, H., Yang, H., Dou, J., Wang, J., & Zhao, P. (2024) · Energies · doi:10.3390/en17184559 · Cited by 12 · see citing works
Self-citation
Automated Fruit Classification Based on Deep Learning Utilizing Yolov8
Dewi, C., Kamlasi, O. M., Chhabra, G., Dai, G., Kaushik, K., & Khan, I. U. (2023) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon59197.2023.10434542 · Cited by 11 · see citing works
Remote sensing data fusion approach for estimating forest degradation: a case study of boreal forests damaged by Polygraphus proximus
Illarionova, S., Tregubova, P., Shukhratov, I., Shadrin, D., Kedrov, A., & Burnaev, E. V. (2024) · Frontiers in Environmental Science · doi:10.3389/fenvs.2024.1412870 · Cited by 11 · see citing works
Thermal canopy segmentation in tomato plants: A novel approach with integration of YOLOv8-C and FastSAM
Hemamalini, P., Chandraprakash, M. K., Laxman, R., RATHINAKUMARI, C., Senthil Kumaran, G., & Suneetha, K. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.100806 · Cited by 11 · see citing works
Detection of Apple Leaf Diseases Based on LightYOLO-AppleLeafDx
Zou, H., Lv, P., & Zhao, M. (2025) · Plants · doi:10.3390/plants14040599 · Cited by 11 · see citing works
Research on Detection Method of Chaotian Pepper in Complex Field Environments Based on YOLOv8
Duan, Y., Li, J., & Zou, C. (2024) · Sensors · doi:10.3390/s24175632 · Cited by 10 · see citing works
TomatoPoseNet: An Efficient Keypoint-Based 6D Pose Estimation Model for Non-Destructive Tomato Harvesting
Ni, J., Zhu, L., Dong, L., Wang, R., Chen, K., Gao, J., Wang, W., Zhou, L., Zhao, B., Rong, J., et al. (2024) · Agronomy · doi:10.3390/agronomy14123027 · Cited by 10 · see citing works
Research on High-Precision Target Detection Technology for Tomato-Picking Robots in Sustainable Agriculture
Song, K., Chen, S., Wang, G., Qi, J., Gao, X., Xiang, M., & Zhou, Z. (2025) · Sustainability · doi:10.3390/su17072885 · Cited by 10 · see citing works
SPHERE: Benchmarking YOLO vs. CNN on a Novel Dataset for High-Accuracy Solar Panel Defect Detection in Renewable Energy Systems
Ayturan, K., Sarıkamış, B., Akşahin, M. F., & Kutbay, U. (2025) · Applied Sciences · doi:10.3390/app15094880 · Cited by 10 · see citing works
YOLO-ODD: an improved YOLOv8s model for onion foliar disease detection
Raj, A., Dawale, M. B., Wayal, S. M., Khandagale, K. S., Bhangare, I., Banerjee, S., Gajarushi, A. S., Velmurugan, R., Baghini, M. S., & GAWANDE, S. J. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1551794 · Cited by 10 · see citing works
Real-Time Strawberry Ripeness Classification and Counting: An Optimized YOLOv8s Framework with Class-Aware Multi-Object Tracking
Ogundele, O. M., Tamrakar, N., Kook, J., Kim, S. M., Choi, J.-I., Karki, S., Akpenpuun, T. D., & Kim, H. T. (2025) · Agriculture · doi:10.3390/agriculture15181906 · Cited by 10 · see citing works
Real-Time Joint-Stem Prediction for Agricultural Robots in Grasslands Using Multi-Task Learning
Li, J., Güldenring, R., & Nalpantidis, L. (2023) · Agronomy · doi:10.3390/agronomy13092365 · Cited by 9 · see citing works
A Honey Bee In-and-Out Counting Method Based on Multiple Object Tracking Algorithm
Lei, C., Lu, Y., Xing, Z., Zhang, J., Li, S., Wu, W., & Liu, S. (2024) · Insects · doi:10.3390/insects15120974 · Cited by 9 · see citing works
CTDA: an accurate and efficient cherry tomato detection algorithm in complex environments
Liang, Z., Zhang, C., Lin, Z., Wang, G., Li, X., & Zou, X. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1492110 · Cited by 9 · see citing works
Advancing construction safety: YOLOv8-CGS helmet detection model
Wu, Z., Lei, X., & Kumar, M. (2025) · PLoS ONE · doi:10.1371/journal.pone.0321713 · Cited by 12 · see citing works
A Precise Plot-Level Rice Yield Prediction Method Based on Panicle Detection
Wei, J., Tian, X., Ren, W., Gao, R. C., Ji, Z., Kong, Q., & Su, Z. (2024) · Agronomy · doi:10.3390/agronomy14081618 · Cited by 9 · see citing works
YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once SeriesPreprint
Sapkota, R., Qureshi, R., Flores-Calero, M. J., Badgujar, C., Nepal, U., Poulose, A., Zeno, P. J., Vaddevolu, U. B. P., Yan, P. H., & Karkee, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4874098 · Cited by 9 · see citing works

YOLOV8-MR: An Improved Lightweight YOLOv8 Algorithm for Tomato Fruit Detection
Li, X., Cai, C., Yang, Y., & Song, B. (2025) · IEEE Access · doi:10.1109/access.2025.3533489 · Cited by 11 · see citing works
AL-YOLOv10: an improved YOLOv10-based model for tomato detection
Xu, X., Liu, J., Zhang, S., & Yang, P. (2025) · doi:10.1117/12.3092169
Lightweight High-Accuracy Tomato Detection and Classification by Efficientnet-Enhanced YOLOv8
Tsukada, H., & Kubota, A. (2025) · International Symposium on Multimedia · doi:10.1109/ism66958.2025.00024
Enhanced tomato detection in greenhouse environments: a lightweight model based on S-YOLO with high accuracy
Sun, X. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1451018 · Cited by 40 · see citing works
D3-YOLOv10: Improved YOLOv10-Based Lightweight Tomato Detection Algorithm Under Facility Scenario
Li, A., Wang, C., Ji, T., Wang, Q., & Zhang, T. (2024) · Agriculture · doi:10.3390/agriculture14122268 · Cited by 53 · see citing works

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cites

Cited by

Related

Record

About these checks