The Fruit Recognition and Evaluation Method Based on Multi-Model Collaboration
Precision agriculture technology based on computer vision is of great significance in fruit recognition and evaluation. In this study, we propose a fruit recognition and evaluation method based on multi-model collaboration. Firstly, the detection model was used to accurately locate and crop the frui...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/994 |
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| author | Mingzheng Huang Dejin Chen Dewang Feng |
| author_facet | Mingzheng Huang Dejin Chen Dewang Feng |
| author_sort | Mingzheng Huang |
| collection | DOAJ |
| description | Precision agriculture technology based on computer vision is of great significance in fruit recognition and evaluation. In this study, we propose a fruit recognition and evaluation method based on multi-model collaboration. Firstly, the detection model was used to accurately locate and crop the fruit area, and then the cropped image was input into the classification module for detailed classification. Finally, the classification results were optimized by the feature matching network. In the method, the detection model was based on YOLOv8, and the model was improved by introducing a TripletAttention structure and an Attention Mechanism-Based Feature Fusion (AFM) structure. The improved YOLOv8 model improves the P, R, mAP50, and MAP50-95 indicators by 2.4%, 2.1%, 1%, and 1.3%, respectively, compared with the baseline model on only one generalized “fruit” label dataset. The classification model Swin Transformer used in this study has a classification accuracy of 92.6% on a dataset of 27 fruit categories, and the feature matching network based on cosine similarity can calibrate the classification results with low confidence. The experimental results show that the proposed method can be applied to the maturity assessment of apples and tomatoes, as well as to the non-destructive testing of apples. |
| format | Article |
| id | doaj-art-d5367176a4644e6a99c9fde631554c2d |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-d5367176a4644e6a99c9fde631554c2d2025-01-24T13:21:36ZengMDPI AGApplied Sciences2076-34172025-01-0115299410.3390/app15020994The Fruit Recognition and Evaluation Method Based on Multi-Model CollaborationMingzheng Huang0Dejin Chen1Dewang Feng2College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaPrecision agriculture technology based on computer vision is of great significance in fruit recognition and evaluation. In this study, we propose a fruit recognition and evaluation method based on multi-model collaboration. Firstly, the detection model was used to accurately locate and crop the fruit area, and then the cropped image was input into the classification module for detailed classification. Finally, the classification results were optimized by the feature matching network. In the method, the detection model was based on YOLOv8, and the model was improved by introducing a TripletAttention structure and an Attention Mechanism-Based Feature Fusion (AFM) structure. The improved YOLOv8 model improves the P, R, mAP50, and MAP50-95 indicators by 2.4%, 2.1%, 1%, and 1.3%, respectively, compared with the baseline model on only one generalized “fruit” label dataset. The classification model Swin Transformer used in this study has a classification accuracy of 92.6% on a dataset of 27 fruit categories, and the feature matching network based on cosine similarity can calibrate the classification results with low confidence. The experimental results show that the proposed method can be applied to the maturity assessment of apples and tomatoes, as well as to the non-destructive testing of apples.https://www.mdpi.com/2076-3417/15/2/994multi-model collaborationfruit recognition and evaluationimproved YOLOv8 detection modelswin transformer classification modelfeature matching network |
| spellingShingle | Mingzheng Huang Dejin Chen Dewang Feng The Fruit Recognition and Evaluation Method Based on Multi-Model Collaboration Applied Sciences multi-model collaboration fruit recognition and evaluation improved YOLOv8 detection model swin transformer classification model feature matching network |
| title | The Fruit Recognition and Evaluation Method Based on Multi-Model Collaboration |
| title_full | The Fruit Recognition and Evaluation Method Based on Multi-Model Collaboration |
| title_fullStr | The Fruit Recognition and Evaluation Method Based on Multi-Model Collaboration |
| title_full_unstemmed | The Fruit Recognition and Evaluation Method Based on Multi-Model Collaboration |
| title_short | The Fruit Recognition and Evaluation Method Based on Multi-Model Collaboration |
| title_sort | fruit recognition and evaluation method based on multi model collaboration |
| topic | multi-model collaboration fruit recognition and evaluation improved YOLOv8 detection model swin transformer classification model feature matching network |
| url | https://www.mdpi.com/2076-3417/15/2/994 |
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