AlodgeNet:一种基于无人机RGB图像的紫花苜蓿倒伏识别方法OA

Aiming at the problems of blurry boundaries,irregular shapes,and difficulty in accurately identifying small-scale lodging areas of Medicago sativa in complex field scenarios,this study proposed a lodging identification method for Medicago sativa based on unmanned air vehicle(UAV)RGB images—the AlodgeNet model.To enhance the model's ability to capture features of irregularly shaped and small-area lodging regions,as well as strengthen the learning of spatial hierarchical structures,the Large Separable Kernel Attention(LSKA)mechanism and Spatial Pyramid Dilated Convolution(SPD-Conv)were introduced into the YOLOv8x-seg network to replace some of the convolution layers in the original network.Meanwhile,in the Yellow River Irrigation Area of Ningxia,RGB images of Medicago sativa lodging at different flight altitudes(5.0 m,7.5 m,10.0 m)and growth stages were collected by UAV,and a dataset was constructed based on these images for model training.The experimental results showed that the AlodgeNet model achieved the best performance in identifying Medicago sativa lodging regions in images collected at a flight altitude of 10.0 m,and its recognition performance for lodging regions in images collected at the early flowering stage was better than that at the branching stage.The precision,recall,mean average precision at the intersection over union(IoU)threshold of 0.50(mAP50),and mean average precision at IoU thresholds ranging from 0.50 to 0.95 with a step size of 0.05(mAP50∶95)of the AlodgeNet model reached 84.9%,79.2%,83.8%,and 56.7%,respectively.Its overall performance outperformed the YOLO v5x-seg,YOLO v10x-seg,YOLO v11x-seg,YOLO v8x-seg,RT-DETR,and MASK-RCNN models.Compared with the original YOLO v8x-seg model,the mAP50 and mAP50∶95 of the AlodgeNet model were improved by 5.8 percentage points and 7.3 percentage points,respectively.This study provides an efficient and convenient monitoring method for the rapid identification and area estimation of Medicago sativa lodging in complex field environments,which is conducive to realizing lodging disaster assessment and supporting management decision-making in precision agriculture.