# The Fruit Recognition and Evaluation Method Based on Multi-Model Collaboration
## 4 Conclusions  
In this paper, detection and classification tasks are handled separately and post-processed using a feature matching network. The model can choose different detection and classification models based on practical needs, allowing for independent optimization to achieve the best overall performance. Since the detection model only needs to identify the broad category of \"fruit\", it can focus more on the target's location and shape features, reducing misclassification and missed detection due to category confusion. The classification model can then make finer distinctions within the detected fruit areas, improving recognition accuracy. The feature matching network can correct classification results and expand the recognized categories when new ones appear.  
Figure 17: Tomato maturity test.  
Figure 18: Nondestructive apple testing.  
When new fruit types need to be added, only the classification model needs to be updated, without retraining the detection model. This modular design makes system expansion easier and more efficient, and it can be extended to maturity detection applications.  
The methods proposed in this paper exhibit good generalization and transferability in fruit detection, effectively reducing the time required for data annotation and improving model accuracy. However, recognition is limited when the fruit shape is irregular. When applied to maturity detection, the model performs well for categories with significant differences in skin color but is less effective when the skin color is unusual.  
The improved YOLOv8 model P, R, mAP50, and MAP50-95 are 2.4%, 2.1%, 1%, and 1.3% higher than the baseline model, respectively. It can realize the location of most fruits with circular and oval shapes. The Swin Transformer classification model can realize the classification of 27 types of fruits. The ripeness test of two fruits and the nondestructive test of one fruit are realized.  
Multi-model collaboration can significantly enhance the overall performance of the model, particularly in terms of accuracy and robustness. However, this process inevitably increases the computational overhead, leading to a decrease in processing speed. Specifically, this performance degradation is primarily due to the computation involved with multiple models and the complex feature fusion process. To address this challenge, future work will focus on optimizing algorithms and leveraging hardware acceleration to alleviate the computational bottlenecks introduced by multi-model collaboration, thereby improving the model's running efficiency while maintaining high performance.  
The authors confirm contribution to the paper as follows: study conception and design: M.H. and D.F.; data collection: M.H. and D.C.; analysis and interpretation of results: M.H.; draft manuscript preparation: M.H. and D.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
I would like to thank all the teachers and students in the laboratory for your help and valuable suggestions during the research process. The cooperation and discussion with you greatly promoted the progress of the research, and thank you for your efforts and support.  
The authors declare no conflicts of interest.