UAV remote sensing imagery-based semantic segmentation approach for lodged rice region

作者：Qiang Chen, Chuang Xia, Yinyan Shi, Xiaochan Wang, Xuekai Huang, Lei Wang, Xiaolei Zhang, Enlai Zheng, Xiaojun Gao, Fei Liu · 发表于：Smart Agricultural Technology · 年份：2025 · DOI：10.1016/j.atech.2025.101689 · 被引用次数：2 · 研究领域：3D Surveying and Cultural Heritage、UAV Applications and Optimization、Remote Sensing and LiDAR Applications

Rice is one of the major staple crops in China, and its yield is closely tied to national food security and farmers’ economic returns. Lodging in rice not only reduces the efficiency of mechanical harvesting but also severely impacts yield and grain quality. Therefore, accurately identifying lodged areas is of great importance. This study proposes a rice lodging detection method based on UAV-acquired multispectral remote sensing imagery. High-resolution, multi-temporal images were collected over paddy fields in Yuhang District, Zhejiang Province, using DJI Mavic 3 M and M300 UAVs. A dataset was constructed via image cropping and data augmentation. Two deep learning models—U-Net with a VGG-16 backbone and DeepLabv3+ with a MobileNetv2 backbone—were compared for semantic segmentation performance. Experimental results show that the U-Net model achieved superior performance on the validation set, with a mean Intersection over Union (MIoU) of 91.57 %, mean Pixel Accuracy (MPA) of 95.83 %, Precision of 95.27 %, Recall of 95.83 %, and training/validation losses of 0.106 and 0.151, respectively, outperforming the DeepLabv3+ model. Additionally, the impact of different training-validation data split ratios was examined. The U-Net model showed better generalization and stability when trained with a 9:1 split compared to an 8:2 split. Furthermore, based on the semantic segmentation results, the area of lodged rice was estimated and compared against ground-truth measurements. The U-Net m...