KFP-YOLO: A Lightweight Detection Model for Korla Fragrant Pear Disease and Pest Detection Toward Edge Deployment

Korla fragrant pear disease and pest detection faces challenges such as significant object scale variation, multi-organ target confusion, and limited computational resources for real-time inference on edge devices. This study presents KFP-YOLO, a lightweight object detector based on YOLO26n, and constructs the Korla Fragrant Pear Disease and Pest Dataset (KFP-PDD), which covers leaves, fruits, and flowers and contains 14,092 original images, 22,735 annotated instances, and 11 healthy, disease, and pest categories. In KFP-YOLO, ADown modules are introduced into the backbone to reduce downsampling redundancy, selected C3k2 blocks are replaced with C3-PD modules combining partial convolution and squeeze-and-excitation attention, and selected feature-fusion nodes are redesigned as CFA modules incorporating coordinate attention. Under identical training and evaluation settings, KFP-YOLO reduces the number of parameters from 2.51 M to 1.93 M and the computational complexity from 5.79 to 4.42 GFLOPs, corresponding to reductions of 23.1% and 23.7%, respectively. On the KFP-PDD test set, KFP-YOLO achieves an mAP@0.5 of 0.9429 and an mAP@0.5:0.95 of 0.7434, compared with 0.9458 and 0.7579 for the YOLO26n baseline. Its inference speed reaches 278.97 FPS on the Jetson AGX Orin platform. In addition, evaluation on an independent external test set containing 1052 images and 3377 annotated instances yields a Precision of 0.8814, a Recall of 0.9077, an mAP@0.5 of 0.9325, and an mAP@0.5:0.95 of 0.7322. These results indicate that KFP-YOLO provides a favorable trade-off between detection accuracy, model complexity, and edge inference efficiency, although its detection accuracy is slightly lower than that of the baseline. The proposed model therefore provides a lightweight candidate for further validation in continuous orchard monitoring applications.