AITP-YOLO: improved tomato ripeness detection model based on multiple strategies
IntroductionThis paper offers a multi-scale AITP-YOLO model, based on the enhanced YOLOv10s model, to address the challenges of difficult identification and frequent misdetection of tomatoes, facilitating ripeness detection under realistic conditions.MethodsA four-head detector incorporates a small...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-05-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1596739/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849731833131958272 |
|---|---|
| author | Wenyuan Huang Wenyuan Huang Yiran Liao Peiling Wang Ziao Chen Ziao Chen Ziqi Yang Ziqi Yang Lijia Xu Jiong Mu Jiong Mu |
| author_facet | Wenyuan Huang Wenyuan Huang Yiran Liao Peiling Wang Ziao Chen Ziao Chen Ziqi Yang Ziqi Yang Lijia Xu Jiong Mu Jiong Mu |
| author_sort | Wenyuan Huang |
| collection | DOAJ |
| description | IntroductionThis paper offers a multi-scale AITP-YOLO model, based on the enhanced YOLOv10s model, to address the challenges of difficult identification and frequent misdetection of tomatoes, facilitating ripeness detection under realistic conditions.MethodsA four-head detector incorporates a small target detection layer, enhancing the model's capacity to identify small targets. Secondly, a multi-scale feature fusion technique employing cross-level features is implemented in the feature fusion layer to amalgamate convolutions of varying sizes, enhancing the model's fusion capacity and generalization proficiency for features of diverse scales. The bounding box loss function is modified to Shape-IoU, with the loss computed by emphasizing the shape and scale of the bounding box, hence enhancing the precision of bounding box regression, expediting model convergence, and augmenting model correctness. Ultimately, the model is compressed via Network Slimming puring,which removes redundant channels while mataining detection accuracy.ResultsThe experimental findings indicate that the enhanced model achieves average precision, accuracy, and recall of 92.6%, 89.7%, and 87.4%, respectively. In comparison to the baseline network YOLOv10s, the model weights are compressed by 7.64%, while average precision, accuracy, and recall are elevated by 4.6%, 5.8%, and 7.3%, respectively.DiscussionThe enhanced model features a reduced model size while exhibiting superior detection capabilities, enabling more efficient and precise recognition of tomato stages amidst complicated backgrounds, hence offering a valuable technical reference for automated tomato harvesting technology. |
| format | Article |
| id | doaj-art-e4bd931b41224e53828fc486a150741d |
| institution | DOAJ |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-e4bd931b41224e53828fc486a150741d2025-08-20T03:08:25ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-05-011610.3389/fpls.2025.15967391596739AITP-YOLO: improved tomato ripeness detection model based on multiple strategiesWenyuan Huang0Wenyuan Huang1Yiran Liao2Peiling Wang3Ziao Chen4Ziao Chen5Ziqi Yang6Ziqi Yang7Lijia Xu8Jiong Mu9Jiong Mu10College of Information Engineering, Sichuan Agricultural University, Yaan, ChinaSichuan Key Laboratory of Agricultural Information Engineering, Yaan, ChinaCollege of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, ChinaCollege of Information Engineering, Sichuan Agricultural University, Yaan, ChinaSichuan Key Laboratory of Agricultural Information Engineering, Yaan, ChinaCountry College of Law, Sichuan Agricultural University, Yaan, ChinaCollege of Information Engineering, Sichuan Agricultural University, Yaan, ChinaSichuan Key Laboratory of Agricultural Information Engineering, Yaan, ChinaCollege of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, ChinaCollege of Information Engineering, Sichuan Agricultural University, Yaan, ChinaSichuan Key Laboratory of Agricultural Information Engineering, Yaan, ChinaIntroductionThis paper offers a multi-scale AITP-YOLO model, based on the enhanced YOLOv10s model, to address the challenges of difficult identification and frequent misdetection of tomatoes, facilitating ripeness detection under realistic conditions.MethodsA four-head detector incorporates a small target detection layer, enhancing the model's capacity to identify small targets. Secondly, a multi-scale feature fusion technique employing cross-level features is implemented in the feature fusion layer to amalgamate convolutions of varying sizes, enhancing the model's fusion capacity and generalization proficiency for features of diverse scales. The bounding box loss function is modified to Shape-IoU, with the loss computed by emphasizing the shape and scale of the bounding box, hence enhancing the precision of bounding box regression, expediting model convergence, and augmenting model correctness. Ultimately, the model is compressed via Network Slimming puring,which removes redundant channels while mataining detection accuracy.ResultsThe experimental findings indicate that the enhanced model achieves average precision, accuracy, and recall of 92.6%, 89.7%, and 87.4%, respectively. In comparison to the baseline network YOLOv10s, the model weights are compressed by 7.64%, while average precision, accuracy, and recall are elevated by 4.6%, 5.8%, and 7.3%, respectively.DiscussionThe enhanced model features a reduced model size while exhibiting superior detection capabilities, enabling more efficient and precise recognition of tomato stages amidst complicated backgrounds, hence offering a valuable technical reference for automated tomato harvesting technology.https://www.frontiersin.org/articles/10.3389/fpls.2025.1596739/fulltarget detectionimage recognitionYOLOv10small target detection headmulti-scaletomato |
| spellingShingle | Wenyuan Huang Wenyuan Huang Yiran Liao Peiling Wang Ziao Chen Ziao Chen Ziqi Yang Ziqi Yang Lijia Xu Jiong Mu Jiong Mu AITP-YOLO: improved tomato ripeness detection model based on multiple strategies Frontiers in Plant Science target detection image recognition YOLOv10 small target detection head multi-scale tomato |
| title | AITP-YOLO: improved tomato ripeness detection model based on multiple strategies |
| title_full | AITP-YOLO: improved tomato ripeness detection model based on multiple strategies |
| title_fullStr | AITP-YOLO: improved tomato ripeness detection model based on multiple strategies |
| title_full_unstemmed | AITP-YOLO: improved tomato ripeness detection model based on multiple strategies |
| title_short | AITP-YOLO: improved tomato ripeness detection model based on multiple strategies |
| title_sort | aitp yolo improved tomato ripeness detection model based on multiple strategies |
| topic | target detection image recognition YOLOv10 small target detection head multi-scale tomato |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1596739/full |
| work_keys_str_mv | AT wenyuanhuang aitpyoloimprovedtomatoripenessdetectionmodelbasedonmultiplestrategies AT wenyuanhuang aitpyoloimprovedtomatoripenessdetectionmodelbasedonmultiplestrategies AT yiranliao aitpyoloimprovedtomatoripenessdetectionmodelbasedonmultiplestrategies AT peilingwang aitpyoloimprovedtomatoripenessdetectionmodelbasedonmultiplestrategies AT ziaochen aitpyoloimprovedtomatoripenessdetectionmodelbasedonmultiplestrategies AT ziaochen aitpyoloimprovedtomatoripenessdetectionmodelbasedonmultiplestrategies AT ziqiyang aitpyoloimprovedtomatoripenessdetectionmodelbasedonmultiplestrategies AT ziqiyang aitpyoloimprovedtomatoripenessdetectionmodelbasedonmultiplestrategies AT lijiaxu aitpyoloimprovedtomatoripenessdetectionmodelbasedonmultiplestrategies AT jiongmu aitpyoloimprovedtomatoripenessdetectionmodelbasedonmultiplestrategies AT jiongmu aitpyoloimprovedtomatoripenessdetectionmodelbasedonmultiplestrategies |