Abstract: To rapidly and accurately identify blueberry fruit maturity under complex field conditions, a lightweight detection model for blueberry maturity (MSC-YOLOv8)was proposed based on YOLOv8n. YOLOv8n, a recent addition to the YOLO series, offers the fastest detection speed and the lightest architecture, albeit with slightly lower recognition accuracy. Considering these attributes, YOLOv8n was chosen as the foundational model for blueberry maturity recognition. Compared to traditional object detection algorithms, YOLOv8 boasts a significantly faster detection speed. By using a single forward propagation to obtain detection results for all targets simultaneously, YOLOv8 achieves low latency, making it suitable for real-time applications. Additionally, YOLOv8 demonstrates high detection accuracy, rivaling advanced object detection algorithms, and excels particularly in detecting small targets. These advantages make YOLOv8 a superior choice for this application. Before training the model, the original blueberry datasets need to be augmented using Deep Convolution Generative Adversarial Networks (DCGAN) to generate additional blueberry images.This aims to generate diverse blueberry images to enhance the datasets, thereby improving the model's capability for detecting and recognizing blueberries in complex field environments. Firstly, to reduce the number of parameters and improve the model's running speed, MobileNetV3 was employed as the backbone feature extraction network, which was conducive to detection in complex field environments. Secondly, to enhance the network's ability to extract features from blueberries, the Convolutional Block Attention Module (CBAM) was integrated into the backbone feature extraction network. Finally, by introducing SCYLLA-IoU (SIoU) as the bounding box regression loss function for YOLOv8n, the issue of angle mismatch between the ground truth and predicted boxes was addressed, further improving the accuracy of blueberry maturity identification. In terms of the backbone feature extraction network, compared with MobileNetV3, ShuffleNet, VanillaNet, and YOLOv8's own backbone CspdarkNet, the experimental results showed that MobileNetV3 as the backbone network of YOLOv8 had the highest mAP value. Additionally, CBAM demonstrated the best mAP performance in various attention mechanisms such as CBAM, SE, ShuffleAttention and MHSA. When assessing bounding box regression loss functions, SIoU outperformed GIoU, EIoU, and CIoU in terms of mAP. Through three key improvements, this study proposed a lightweight network model, MSC-YOLOv8, for blueberry fruit maturity detection,ensuring the real-time performance and accuracy. The results showed that the improved MSC-YOLOv8 model was 3.9 percentage points higher than that of YOLOv8. The average detection time was reduced by 3.97 ms compared with the original YOLOv8. Compared with SSD and CenterNet models, the improved model achieved better results on the blueberry dataset, and the average precision mean mAP is increased by 4.6 and 1.1 percentage points, respectively, which has advantages in detection speed and accuracy. This research provides technical support for blueberry picking robots to perform picking work under complex conditions in the fields.