Aiming at the key technical challenges of complex background noise interference, fruit mutual occlusion, and multi-scale object recognition in natural scene Korla pear detection tasks, an improved YoloV11 object detection algorithm integrating the Efficient Channel Attention (ECA) mechanism and Bidirectional Feature Pyramid Network (BiFPN) is proposed (ECABiFPN-YOLOv11). By introducing the ECA module to adaptively optimize feature channel weights and combining the BiFPN architecture to achieve efficient cross-level feature fusion, the model’s perception and expression capabilities for multi-scale features of Korla pear objects are significantly enhanced. The experimental results show that the improved model reaches 86.8% on the mean average precision (mAP50) index, which is 4.7 percentage points higher than that of the original YoloV11 (82.1%). The mAP@0.5:0.95 value is 62.7%, which is 4.4% higher than that of the original model. The training box_loss (final) value is 3.7% lower than that of the original model, and the verification box_loss (final) value is 3.6% lower than that of the original model. These results provide reliable technical support for the research and development of automatic grading and sorting of fragrant pear fruits and intelligent picking systems in the field of smart agriculture.