## 1 Introduction  
Blueberries are widely grown around the world, including the United States, Japan, China, New Zealand, and Europe [1], and have high economic value because of their rich nutrition and unique taste [2]. As traditional manual harvesting can no longer meet the growing market demands, automated blueberry harvesting technology has become a research interest [3]. Fast and accurate maturity detection is key to automated picking but faces challenges in complex outdoor environments, such as shading by branches and leaves, variations in light, overlapping fruits, and the small diameter of blueberries themselves. Traditional image processing methods mainly rely on extracting surface features, such as the color and texture of the target fruit, and then use support vector machines and K-means clustering algorithms to detect and classify the maturity of the fruit [4]. [PERSON] et al. classified apricots into three maturity stages (i.e., unripe, ripe, and overripe), employing color features [5]. [PERSON] et al. used the K-Nearest Neighbors(KNN) method based on color feature extraction to classify the fruit maturity of oil palms [6]. [PERSON] et al. studied starfruit maturity using smartphone-based imaging and a variety of traditional machine-learning algorithms [7]. [PERSON] et al. predicted the maturity of grapes from color values using the back-propagation neural network algorithm (BPNN) [8]. [PERSON] et al. developed three strawberry maturity recognition models using absorbance data, finding the multivariate nonlinear model most accurate in greenhouse conditions [9]. [PERSON] et al. segmented strawberries based using hue-based masks and used the red ratio to quantify maturity [10]. While traditional methods achieve high accuracy in controlled settings, their sensitivity to lighting and background changes limits their generalization and suitability for outdoor farming, and this limitation has led to the adoption of deep learning, offering a more robust approach for maturity detection in fruit and vegetable crops under complex natural conditions [3]. [PERSON] et al. proposed a strawberry maturity grading model based on YOLOv8, combined with a LW-Swin Transformer module to improve the generalization capability [11]. Addressing more complex scenarios, such as the detection of small objects and the occlusion of different maturity stages, [PERSON] et al. proposed a foreground-foreground class balance method and an improved YOLOv8s network to detect and segment tomato fruits [12]. [PERSON] et al. [13] compared strawberry maturity classification using YOLOv3 with aerial and ground-based images, demonstrating superior speed and accuracy, respectively. [PERSON] et al. [14] addressed the problem of limited training data for pineapple maturity grading using the more traditional Mask R-CNN. [PERSON] et al. [15] introduced a dual-path lightweight CNN combining regular and dilated convolution to classify olive varieties. To address occlusion and lighting interference in broccoli maturity recognition, [PERSON] et al. [16] developed a CNN-Transformer-based instance segmentation network. [PERSON] et al. [17] compared six YOLO variants for wild blueberry maturity detection, aiding localized grower decisions. [PERSON] et al. [18] proposed a Faster R-CNN model with ResNet-50 for detecting coconut maturity in complex backgrounds. [PERSON] et al. [19] combined a Faster R-CNN with a path aggregation network for tomato maturity detection, achieving strong results in occluded and uneven lighting conditions. [PERSON] et al. [20] introduced a multi-task variant of YOLOv7 to assess cherry tomato with respect to the location, size, category, and maturity of the fruit clusters. The methods and drawbacks of fruit and vegetable maturity detection research using deep learning are shown in Table 1.