BAP-YOLO: A Pear Disease and Pest Detection Algorithm for Natural Orchard Environments

Pest and disease detection in natural pear orchard environments is often affected by branch and leaf occlusion, non-uniform illumination, and complex background textures. In addition, substantial variations exist in the morphology and scale of lesions and insect-damaged regions, while the weak texture features of early-stage lesions can be progressively attenuated during feature propagation. Therefore, effectively representing targets at different scales while preserving discriminative local details and suppressing irrelevant background information is crucial for improving pest and disease detection in pear orchards. To address these challenges, this study proposes BAP-YOLO, a detection framework designed for natural pear orchard environments. The framework progressively enhances feature representation through context aggregation, adaptive scale modeling, and discriminative detail refinement. First, a Branch-based Target Enhancement Fusion (BTEF) module is introduced into the backbone network to enhance hierarchical feature representations at multiple backbone stages, thereby reducing interference from complex background information in target representation. Second, an Adaptive Scale Dynamic (ASD) module is designed and embedded into the neck network to construct candidate features with different effective receptive fields and dynamically modulate their contributions using multi-resolution statistics, thereby improving the model’s ability to detect targets with substantial scale variations. Finally, a Parallel Nonlinear Enhancement (PNE) module is incorporated between the neck network and detection head to enrich weak-texture and local-detail representations through complementary nonlinear transformations and element-wise fusion before prediction. Experiments were conducted on a pear orchard image dataset containing six classes: beetle, black_block, dry blight, health, rust, and scab. The experimental results showed that BAP-YOLO achieved a precision of 0.854, an mAP@50 of 0.876, and an F1-score of 0.826. Compared with the baseline YOLO11n, BAP-YOLO improved Precision, mAP@50, and F1-score by 6.1, 2.1, and 1.3 percentage points, respectively. These results demonstrate the potential of BAP-YOLO for pear disease and pest detection in natural orchard images.