MATE-YOLO: Multi-scale Attention Task-aligned Enhanced Detection Network for Apple Fruit in Complex Orchard Environments

Accurate apple detection in complex orchards remains challenging due to foliage occlusion, illumination variations, and cluttered backgrounds. This study proposes an enhanced YOLOv11n framework integrating three architectural innovations. First, the EMCSP (EMA-enhanced Cross-Stage Partial) module is introduced into the backbone, synergistically incorporating multi-scale attention within cross-stage partial topology to strengthen discriminative feature extraction. Second, the ELA-HSFPN (Efficient Local Attention enhanced Hierarchical Scale Feature Pyramid Network) is devised for the neck, leveraging decoupled spatial attention and bidirectional hierarchical fusion to enhance multi-scale representation. Third, the TADDH (Task-Aligned Dynamic Detection Head) supersedes the conventional head, employing task decomposition, dynamic deformable convolution, and probabilistic feature modulation to achieve optimal classification-localization alignment. Extensive experiments demonstrate substantial improvements over baseline YOLOv11n: Precision+1.4%, Recall+2.3%, mAP@0.5+3.0%, and mAP@0.5:0.95 +1.7%. These results validate the efficacy of our methodology for intelligent fruit harvesting applications.