In the era of smart agriculture, intelligent fruit maturity detection has become a critical task. However, in complex orchard environments, factors such as occlusion by branches and leaves and interference from bagging materials pose significant challenges to detection accuracy. To address this issue, this study focuses on maturity detection of ‘Jiang’ pomegranates and proposes an improved YOLO-AMAS algorithm. The method integrates an Adaptive Feature Enhancement (AFE) module, a Multi-Scale Convolutional Attention Module (MSCAM), and an Adaptive Spatial Feature Fusion (ASFF) module. The AFE module effectively suppresses complex backgrounds through dual-channel spatial attention mechanisms; the MSCAM enhances multi-scale feature extraction capability using a pyramidal spatial convolution structure; and the ASFF optimizes the representation of both shallow details and deep semantic information via adaptive weighted fusion. A SlideLoss function based on Intersection over Union is introduced to alleviate class imbalance. Experimental validation conducted on a dataset comprising 6564 images from multiple scenarios demonstrates that the YOLO-AMAS model achieves a precision of 90.9%, recall of 86.0%, mAP@50 of 94.1% and mAP@50:95 of 67.6%. The model significantly outperforms mainstream detection models including RT-DETR-1, YOLOv3 to v6, v8, and 11 under multi-object, single-object, and occluded scenarios, with a mAP50 of 96.4% for bagged mature fruits. Through five-fold cross-validation, the model’s strong generalization capability and stability were demonstrated. Compared to YOLOv8, YOLO-AMAS reduces the false detection rate by 30.3%. This study provides a reliable and efficient solution for intelligent maturity detection of ‘Jiang’ pomegranates in complex orchard environments.