## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **Methods** & **Shortcomings** \\ \hline [PERSON] et al. [11] & YOLOv8-based strawberry maturity grading with LW-Swin & Combining YOLOv8 and Transformer modules to achieve generalization will increase complexity. \\ \hline [PERSON] et al. [12] & Foreground–foreground class balance and YOLOv8s for tomato fruit segmentation & Foreground–foreground class balance does not work well for highly occluded fruits \\ \hline [PERSON] et al. [13] & YOLOv3 for strawberry maturity classification using aerial and ground images & The accuracy is lower when using aerial images in complex environments. \\ \hline [PERSON] et al. [14] & Mask R-CNN for pineapple maturity grading with limited training data & Limited training data for Mask R-CNN hinders model performance \\ \hline [PERSON] et al. [15] & Dual-path lightweight CNN with regular and dilated convolutions for olive variety classification & The complex dual-path CNN architecture will lead to high computational costs. \\ \hline [PERSON] et al. [16] & CNN-Transformer-based instance segmentation network for broccoli maturity recognition & The model performs poorly in detection under lighting and occlusion conditions. \\ \hline [PERSON] et al. [17] & Comparison of six YOLO variants for wild blueberry maturity detection & Lack of a focused solution for wild blueberry environments in real-time applications \\ \hline [PERSON] et al. [18] & Faster R-CNN with ResNet-50 for coconut maturity detection in complex backgrounds & Challenges with detecting coconut maturity in diverse backgrounds under varying conditions \\ \hline [PERSON] et al. [19] & Faster R-CNN with path aggregation network for tomato maturity detection & Detecting tomato maturity is very difficult under occlusion and poor lighting, and the model takes a long time to train \\ \hline [PERSON] et al. [20] & YOLOv7 multi-task variant for cherry tomato maturity assessment & Multi-task nature of the model increases computational complexity \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Methods and drawbacks in fruit and vegetable maturity detection using deep learning. While these algorithms outperform traditional computer vision methods in accuracy, robustness, and applicability, they are often limited to close-range detection and struggle with more complex scenarios. In blueberry maturity detection, the issues of extraordinarily small fruits, severe occlusions, and strongly varying light conditions need to be addressed for automated harvesting across large areas. Therefore, how to optimize model complexity while ensuring detection accuracy and enhancing adaptability in complex environments remains a key issue in current algorithm research. The contributions of this work are as follows: (1) This article extends the well-known YOLOv8n model by Receptive-Field Attention Convolution (RFAConv) [21] in the backbone network to enhance the model's ability to extract small object features and improve detection accuracy. (2) A small object detection layer is introduced in the neck network. (3) These features are integrated as a four-head adaptive spatial feature fusion (Detect-FASFF), based on [22], which further enhances the model's ability to detect targets at different scales and reduces feature loss to solve the problem of missed detections and false positives under conditions such as long-range views, severe occlusion, and varying lighting. (4) A more focused complete intersection over union loss (Focaler-CIoU) is introduced into the loss function to speed up the convergence of the model, thus improving its detection performance [23].