ITD-YOLO: An Improved YOLO Model for Impurities in Premium Green Tea Detection
During the harvesting and preparation of tea, it is common for tea to become mixed with some impurities. Eliminating these impurities is essential to improve the quality of famous green tea. At present, this sorting procedure heavily depends on manual efforts, which include high labor intensity, low...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Foods |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-8158/14/9/1554 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850137646132625408 |
|---|---|
| author | Zezhong Ding Yanfang Li Bin Hu Zhiwei Chen Houzhen Jia Yali Shi Xingmin Zhang Xuesong Zhu Wenjie Feng Chunwang Dong |
| author_facet | Zezhong Ding Yanfang Li Bin Hu Zhiwei Chen Houzhen Jia Yali Shi Xingmin Zhang Xuesong Zhu Wenjie Feng Chunwang Dong |
| author_sort | Zezhong Ding |
| collection | DOAJ |
| description | During the harvesting and preparation of tea, it is common for tea to become mixed with some impurities. Eliminating these impurities is essential to improve the quality of famous green tea. At present, this sorting procedure heavily depends on manual efforts, which include high labor intensity, low sorting efficiency, and high sorting costs. In addition, the hardware performance is poor in actual production, and the model is not suitable for deployment. To solve this technical problem in the industry, this article proposes a lightweight algorithm for detecting and sorting impurities in premium green tea in order to improve sorting efficiency and reduce labor intensity. A custom dataset containing four categories of impurities was created. This dataset was employed to evaluate various YOLOv8 models, ultimately leading to the selection of YOLOv8n as the base model. Initially, four loss functions were compared in the experiment, and Focaler_mpdiou was chosen as the final loss function. Subsequently, this loss function was applied to other YOLOv8 models, leading to the selection of YOLOv8m-Focaler_mpdiou as the teacher model. The model was then pruned to achieve a lightweight model at the expense of detection accuracy. Finally, knowledge distillation was applied to enhance its detection performance. Compared to the base model, it showed advancements in P, R, mAP, and FPS by margins of 0.0051, 0.0120, and 0.0094 and an increase of 72.2 FPS, respectively. Simultaneously, it achieved a reduction in computational complexity with GFLOPs decreasing by 2.3 and parameters shrinking by 860350 B. Afterwards, we further demonstrated the model’s generalization ability in black tea samples. This research contributes to the technological foundation for sophisticated impurity classification in tea. |
| format | Article |
| id | doaj-art-d15b452d449e4eed9d2169fc4d413027 |
| institution | OA Journals |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj-art-d15b452d449e4eed9d2169fc4d4130272025-08-20T02:30:46ZengMDPI AGFoods2304-81582025-04-01149155410.3390/foods14091554ITD-YOLO: An Improved YOLO Model for Impurities in Premium Green Tea DetectionZezhong Ding0Yanfang Li1Bin Hu2Zhiwei Chen3Houzhen Jia4Yali Shi5Xingmin Zhang6Xuesong Zhu7Wenjie Feng8Chunwang Dong9Tea Research Institute, Information and Economy Institution, Shandong Academy of Agricultural Sciences, Jinan 250100, ChinaWeifang Engineering Vocational College, Weifang 262500, ChinaCollege of Mechanical and Electronic Engineering, Shihezi University, Shihezi 832000, ChinaTea Research Institute, Information and Economy Institution, Shandong Academy of Agricultural Sciences, Jinan 250100, ChinaTea Research Institute, Information and Economy Institution, Shandong Academy of Agricultural Sciences, Jinan 250100, ChinaTea Research Institute, Information and Economy Institution, Shandong Academy of Agricultural Sciences, Jinan 250100, ChinaTea Research Institute, Information and Economy Institution, Shandong Academy of Agricultural Sciences, Jinan 250100, ChinaTea Research Institute, Information and Economy Institution, Shandong Academy of Agricultural Sciences, Jinan 250100, ChinaTea Research Institute, Information and Economy Institution, Shandong Academy of Agricultural Sciences, Jinan 250100, ChinaTea Research Institute, Information and Economy Institution, Shandong Academy of Agricultural Sciences, Jinan 250100, ChinaDuring the harvesting and preparation of tea, it is common for tea to become mixed with some impurities. Eliminating these impurities is essential to improve the quality of famous green tea. At present, this sorting procedure heavily depends on manual efforts, which include high labor intensity, low sorting efficiency, and high sorting costs. In addition, the hardware performance is poor in actual production, and the model is not suitable for deployment. To solve this technical problem in the industry, this article proposes a lightweight algorithm for detecting and sorting impurities in premium green tea in order to improve sorting efficiency and reduce labor intensity. A custom dataset containing four categories of impurities was created. This dataset was employed to evaluate various YOLOv8 models, ultimately leading to the selection of YOLOv8n as the base model. Initially, four loss functions were compared in the experiment, and Focaler_mpdiou was chosen as the final loss function. Subsequently, this loss function was applied to other YOLOv8 models, leading to the selection of YOLOv8m-Focaler_mpdiou as the teacher model. The model was then pruned to achieve a lightweight model at the expense of detection accuracy. Finally, knowledge distillation was applied to enhance its detection performance. Compared to the base model, it showed advancements in P, R, mAP, and FPS by margins of 0.0051, 0.0120, and 0.0094 and an increase of 72.2 FPS, respectively. Simultaneously, it achieved a reduction in computational complexity with GFLOPs decreasing by 2.3 and parameters shrinking by 860350 B. Afterwards, we further demonstrated the model’s generalization ability in black tea samples. This research contributes to the technological foundation for sophisticated impurity classification in tea.https://www.mdpi.com/2304-8158/14/9/1554teaimpurity identificationdeep learningFocaler_mpdioumodel pruningknowledge distillation |
| spellingShingle | Zezhong Ding Yanfang Li Bin Hu Zhiwei Chen Houzhen Jia Yali Shi Xingmin Zhang Xuesong Zhu Wenjie Feng Chunwang Dong ITD-YOLO: An Improved YOLO Model for Impurities in Premium Green Tea Detection Foods tea impurity identification deep learning Focaler_mpdiou model pruning knowledge distillation |
| title | ITD-YOLO: An Improved YOLO Model for Impurities in Premium Green Tea Detection |
| title_full | ITD-YOLO: An Improved YOLO Model for Impurities in Premium Green Tea Detection |
| title_fullStr | ITD-YOLO: An Improved YOLO Model for Impurities in Premium Green Tea Detection |
| title_full_unstemmed | ITD-YOLO: An Improved YOLO Model for Impurities in Premium Green Tea Detection |
| title_short | ITD-YOLO: An Improved YOLO Model for Impurities in Premium Green Tea Detection |
| title_sort | itd yolo an improved yolo model for impurities in premium green tea detection |
| topic | tea impurity identification deep learning Focaler_mpdiou model pruning knowledge distillation |
| url | https://www.mdpi.com/2304-8158/14/9/1554 |
| work_keys_str_mv | AT zezhongding itdyoloanimprovedyolomodelforimpuritiesinpremiumgreenteadetection AT yanfangli itdyoloanimprovedyolomodelforimpuritiesinpremiumgreenteadetection AT binhu itdyoloanimprovedyolomodelforimpuritiesinpremiumgreenteadetection AT zhiweichen itdyoloanimprovedyolomodelforimpuritiesinpremiumgreenteadetection AT houzhenjia itdyoloanimprovedyolomodelforimpuritiesinpremiumgreenteadetection AT yalishi itdyoloanimprovedyolomodelforimpuritiesinpremiumgreenteadetection AT xingminzhang itdyoloanimprovedyolomodelforimpuritiesinpremiumgreenteadetection AT xuesongzhu itdyoloanimprovedyolomodelforimpuritiesinpremiumgreenteadetection AT wenjiefeng itdyoloanimprovedyolomodelforimpuritiesinpremiumgreenteadetection AT chunwangdong itdyoloanimprovedyolomodelforimpuritiesinpremiumgreenteadetection |