journal article·2025-04-30Open access

YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11

Qian Wu, Heming Huang, Dongke Song, Jie Zhou

Applied Sciences· Volume 15· Issue 9

Abstract

Accurate tomato maturity detection represents a critical challenge in precision agriculture. A YOLOv11-based algorithm named YOLO-PGC is proposed in this study for tomato maturity detection. Its three innovative components are denoted by “PGC”, respectively representing the Polarization State Space Strategy with Dynamic Weight Allocation, the Global Horizontal–Vertical Context Module, and the Convolutional–Inductive Feature Fusion Module. The Polarization Strategy enhances robustness against occlusion through adaptive feature importance modulation, he Global Context Module integrates cross-dimensional attention mechanisms with hierarchical feature extraction, and the Convolutional–Inductive Feature Fusion Module employs multimodal integration for improved object discrimination in complex scenes. Experimental results demonstrate that YOLO-PGC achieves superior precision and mean average precision compared to state-of-the-art methods. Validation on the COCO benchmark confirms the framework’s generalization capabilities, maintaining computational efficiency for real-time deployment. YOLO-PGC establishes new performance standards for agricultural object detection with potential applications in similar computer vision challenges. Overall, these components and strategies are integrated into YOLO-PGC to achieve robust object detection in complex scenarios.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Smart Agriculture and AI · Spectroscopy and Chemometric Analyses · Advanced Chemical Sensor Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

The School of Computer, Qinghai Normal University, Xining 810008, China

Shandong Facility Horticulture Bioengineering Research Center, Weifang University of Science and Technology, Weifang 262700, China

The State Key Laboratory of Tibetan Intelligence, Xining 810008, China

The Ministry of Education Key Laboratory of Tibetan Information Processing, Xining 810008, China

Funding

Natural Science Foundation of Qinghai Province, China · National Natural Science Foundation of China · ‘111’Project, China · McCollum Endowed Chair startup fund at Baylor University, China

References (53)

Show source-provided references
1. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
2. Girshick, R. (2015, January 7–13). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.169 ↗
3. Cheng, H., Damerow, L., Sun, Y., and Blanke, M. (2017). Early Yield Prediction Using Image Analysis of Apple Fruit and Tree Canopy Features with Neural Networks. J. Imaging, 3.10.3390/jimaging3010006 ↗
4. Mature Tomato Fruit Detection Algorithm Based on Improved HSV and Watershed Algorithm10.1016/j.ifacol.2018.08.183 ↗
5. Precision Agriculture through Deep Learning: Tomato Plant Multiple Diseases Recognition with CNN and Improved YOLOv710.1109/ACCESS.2024.3383154 ↗
6. Shoaib, M., Hussain, T., Shah, B., Ullah, I., Shah, S.M., Ali, F., and Park, S.H. (2022). Deep Learning-Based Segmentation and Classification of Leaf Images for Detection of Tomato Plant Disease. Front. Plant Sci., 13.10.3389/fpls.2022.1031748 ↗
7. Kavita, M., and Mathur, P. (2020, January 30–31). Crop Yield Estimation in India Using Machine Learning. Proceedings of the 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India.10.1109/ICCCA49541.2020.9250915 ↗
8. Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv.
9. Jocher, G., Chaurasia, A., and Qiu, J. (2024, June 01). Ultralytics YOLOv8. 2023. Available online: https://github.com/ultralytics/ultralytics.
10. Lv, W., Zhao, Y., Chang, Q., Huang, K., Wang, G., and Liu, Y. (2024). RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer. arXiv.
11. Application of Deep Learning Algorithms in Geotechnical Engineering: A Short Critical Review10.1007/s10462-021-09967-1 ↗
12. Wang, D., Cao, W., Zhang, F., Li, Z., Xu, S., and Wu, X. (2022). A Review of Deep Learning in Multiscale Agricultural Sensing. Remote Sens., 14.10.3390/rs14030559 ↗
13. Khanam, R., and Hussain, M. (2024). YOLOv11: An Overview of the Key Architectural Enhancements. arXiv.
14. Badgujar, C.M., Poulose, A., and Gan, H. (2024). Agricultural Object Detection with You Only Look Once (YOLO) Algorithm: A Bibliometric and Systematic Literature Review. arXiv.10.1016/j.compag.2024.109090 ↗
15. Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., and McCool, C. (2016). DeepFruits: A Fruit Detection System Using Deep Neural Networks. Sensors, 16.10.3390/s16081222 ↗
16. Object Detection in Images Using Deep Neural Networks for Agricultural Machinery10.1088/1755-1315/988/3/032002 ↗
17. Khalid, S., Oqaibi, H.M., Aqib, M., and Hafeez, Y. (2023). Small Pests Detection in Field Crops Using Deep Learning Object Detection. Sustainability, 15.10.3390/su15086815 ↗
18. “Tomato-Village”: A Dataset for End-to-End Tomato Disease Detection in a Real-World Environment10.1007/s00530-023-01158-y ↗
19. Chapter Three - Advances in Structured Light Sensors Applications in Precision Agriculture and Livestock Farming10.1016/bs.agron.2015.05.002 ↗
20. Yu, Y., Zhou, Q., Wang, H., Lv, K., Zhang, L., Li, J., and Li, D. (2024). LP-YOLO: A Lightweight Object Detection Network Regarding Insect Pests for Mobile Terminal Devices Based on Improved YOLOv8. Agriculture, 14.10.3390/agriculture14081420 ↗
21. Lippi, M., Bonucci, N., Carpio, R.F., Contarini, M., Speranza, S., and Gasparri, A. (2021, January 22–25). A YOLO-Based Pest Detection System for Precision Agriculture. Proceedings of the 2021 29th Mediterranean Conference on Control and Automation (MED), Puglia, Italy.10.1109/MED51440.2021.9480344 ↗
22. Lightweight Tomato Real-Time Detection Method Based on Improved YOLO and Mobile Deployment10.1016/j.compag.2023.107625 ↗
23. Principles, Developments and Applications of Computer Vision for External Quality Inspection of Fruits and Vegetables: A Review10.1016/j.foodres.2014.03.012 ↗
24. Deep Learning in Agriculture: A Survey10.1016/j.compag.2018.02.016 ↗
25. Faster R-CNN for Multi-Class Fruit Detection Using a Robotic Vision System10.1016/j.comnet.2019.107036 ↗
26. Liu, Y., Wei, C., Yoon, S.-C., Ni, X., Wang, W., Liu, Y., Wang, D., Wang, X., and Guo, X. (2024). Development of Multimodal Fusion Technology for Tomato Maturity Assessment. Sensors, 24.10.3390/s24082467 ↗
27. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23–28). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.10.1109/CVPR.2014.81 ↗
28. Apple Detection During Different Growth Stages in Orchards Using the Improved YOLO-V3 Model10.1016/j.compag.2019.01.012 ↗
29. Deep Learning for Real-Time Fruit Detection and Orchard Fruit Load Estimation: Benchmarking of ‘MangoYOLO’10.1007/s11119-019-09642-0 ↗
30. Zhang, Y., Guo, Z., Wu, J., Tian, Y., Tang, H., and Guo, X. (2022). Real-Time Vehicle Detection Based on Improved YOLOv5. Sustainability, 14.10.3390/su141912274 ↗
31. DeepFruit: A Dataset of Fruit Images for Fruit Classification and Calories Calculation10.1016/j.dib.2023.109524 ↗
32. An Improved YOLOv8 to Detect Moving Objects10.1109/ACCESS.2024.3393835 ↗
33. YOLOv10: Real-Time End-to-End Object Detection
34. Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8–14). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_1 ↗
35. Liu, Z., Gao, J., Yang, G., Zhang, H., and He, Y. (2016). Localization and Classification of Paddy Field Pests Using a Saliency Map and Deep Convolutional Neural Network. Sci. Rep., 6.10.1038/srep20410 ↗
36. Gu, A., Goel, K., and Ré, C. (2021). Efficiently Modeling Long Sequences with Structured State Spaces. arXiv.
37. Deep Spatial-Temporal Feature Fusion for Facial Expression Recognition in Static Images10.1016/j.patrec.2017.10.022 ↗
38. Fu, H., Guo, Z., Feng, Q., Xie, F., Zuo, Y., and Li, T. (2024). MSOAR-YOLOv10: Multi-Scale Occluded Apple Detection for Enhanced Harvest Robotics. Horticulturae, 10.10.3390/horticulturae10121246 ↗
39. Afzaal, U., Bhattarai, B., Pandeya, Y.R., and Lee, J. (2021). An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN. Sensors, 21.10.3390/s21196565 ↗
40. Object Detection in 20 Years: A Survey10.1109/JPROC.2023.3238524 ↗
41. Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S.Z. (2020, January 13–19). Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR42600.2020.00978 ↗
42. Ko, K., Jang, I., Choi, J.H., Lim, J.H., and Lee, D.U. (2021). Stochastic Decision Fusion of Convolutional Neural Networks for Tomato Ripeness Detection in Agricultural Sorting Systems. Sensors, 21.10.3390/s21030917 ↗
43. Smart Solutions for Capsicum Harvesting: Unleashing the Power of YOLO for Detection, Segmentation, Growth Stage Classification, Counting, and Real-Time Mobile Identification10.1016/j.compag.2024.108832 ↗
44. Wu, J., Zhang, B., Zhou, J., Xiong, Y., Gu, B., and Yang, X. (2019). Automatic Recognition of Ripening Tomatoes by Combining Multi-Feature Fusion with a Bi-Layer Classification Strategy for Harvesting Robots. Sensors, 19.10.3390/s19030612 ↗
45. Wang, S., Jiang, H., Yang, J., Ma, X., Chen, J., Li, Z., and Tang, X. (2024). Lightweight Tomato Ripeness Detection Algorithm Based on the Improved RT-DETR. Front. Plant Sci., 15.10.3389/fpls.2024.1415297 ↗
46. Greenhouse Tomato Detection and Pose Classification Algorithm Based on Improved YOLOv510.1016/j.compag.2023.108519 ↗
47. Vmamba: Visual State Space Model
48. Ni, Z., Chen, X., Zhai, Y., Tang, Y., and Wang, Y. (October, January 29). Context-Guided Spatial Feature Reconstruction for Efficient Semantic Segmentation. Proceedings of the European Conference on Computer Vision, Milan, Italy.
49. Chen, J., Kao, S.-H., He, H., Zhuo, W., Wen, S., Lee, C.-H., Chan, S.-H. (2023, January 17–24). Run, Don’t Walk: Chasing Higher FLOPS for Faster Neural Networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.10.1109/CVPR52729.2023.01157 ↗
50. Jocher, G., Stoken, A., Borovec, J., Liu, C., Hogan, A., Diaconu, L., Poznanski, J., Yu, L., Rai, P., and Ferriday, R. (2024, January 15). ultralytics/yolov5: v3.0. Zenodo 2020. Available online: https://zenodo.org/records/3983579.
51. Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv.
52. Yang, S., Cao, Z., Liu, N., Sun, Y., and Wang, Z. (2024). Maritime Electro-Optical Image Object Matching Based on Improved YOLOv9. Electronics, 13.10.3390/electronics13142774 ↗
53. YOLO-TLA: An Efficient and Lightweight Small Object Detection Model Based on YOLOv510.1007/s11554-024-01519-4 ↗