# The Fruit Recognition and Evaluation Method Based on Multi-Model Collaboration
## 1 Introduction  
Today, labor costs in the fruit production process still account for a significant portion of the total cost. Therefore, researching intelligent and automated fruit production processes is of great significance and practical value. Fruit recognition technology with high accuracy and broad adaptability can not only accurately identify and classify different types of fruits, helping supermarkets and retail stores with automated fruit vending, but it also plays an important role in yield estimation, automated harvesting, and ripeness detection. With the development of computer vision technology, various machine vision detection algorithms have been widely applied in fruit recognition and evaluation. [PERSON] et al. [1] used two color descriptors, the red-green difference and the red-green ratio, to detect different ripening stages of tomatoes, enabling the assessment of tomato ripeness. [PERSON] et al. [2] utilized color and texture features to train a random forest binary classification model, effectively distinguishing between different ripeness stages of lychees. [PERSON] et al. [3] used local binary pattern features and hierarchical contour analysis, utilizing texture and intensity distribution to identify [PERSON] citrus fruits. Traditional machine learning methods primarily rely on extracting features such as color, shape, and texture from fruit images to achieve fruit recognition and ripeness evaluation. However, since manually designedfeatures often struggle to handle complex scenes with mixed backgrounds and varying environmental factors, it is difficult for them to meet the needs of intelligent and automated fruit recognition and evaluation. Deep learning-based models can automatically extract relevant features without manual intervention, making them more widely applicable in the agricultural field. [PERSON] et al. [4] proposed an improved Mask R-CNN visual detector model, which enables the faster and more accurate recognition of overlapping apples. [PERSON] et al[PERSON] [5] improved the YOLOv4 model by adding coordinate attention modules to both the feature extraction module and the feature pyramid, which enhanced the accuracy of fruit recognition. [PERSON] et al. [6] introduced a transformer-based BRA sparse attention module into the backbone network of YOLOv8 and improved the detection head and feature fusion network to achieve mango recognition[PERSON] [PERSON] et al[PERSON] [7] improved the performance of the YOLOv4 model in apple ripeness assessment tasks by adding a convolutional attention mechanism module to the detector. [PERSON] et al. [8] used the SSD network and MobileNetV2 network for the recognition and freshness assessment of three types of fruits: apples, oranges, and bananas. [PERSON] et al. [9] utilized a Feature Pyramid Network and dilated spatial pyramid pooling to achieve apple detection in orchards. [PERSON] et al. [10] proposed a fruit image classification framework, where the first part uses a convolutional autoencoder (CAE) for pretraining the images, and the second part employs an attention-based DenseNet to extract features from the images. [PERSON] et al[PERSON] [11] aimed to detect citrus fruits and assess fruit ripeness. In the first stage, YOLOv5 was used to recognize citrus fruits in the images. In the second stage, an improved visual saliency detection algorithm was applied to generate saliency maps of the fruits, which combined RGB image information with saliency maps. A ResNet34 network was then used to determine the ripeness level of the fruits. Deep learning classification models have gradually evolved from traditional CNN-based models (such as ResNet [12], EfficientNet [13], etc.) to Transformer-based classification models. Vision Transformer [14] directly applies the self-attention mechanism to the entire image, effectively handling long-range dependencies, but it comes with high computational cost.