A Multi-Scale Rice Lodging Monitoring Method Based on MSR-Lodfnet

作者：Xinle Zhang, Xinyi Han, Chuan Qin, Zhenghua An, Beisong Qi, Jiming Liu, Buyun Du, Huanjun Liu, Yihao Wang, Ling‐Hua Meng, Chao Wang · 发表于：Agriculture · 年份：2025 · DOI：10.3390/agriculture15232487 · 被引用次数：2 · 研究领域：Advanced Technologies in Various Fields、UAV Applications and Optimization、Indoor and Outdoor Localization Technologies

Rice lodging is a major agricultural disaster that reduces yield and quality. Accurate lodging detection and causal analysis are essential for disaster mitigation and precision management. To overcome the limited coverage and low automation of conventional approaches, we propose MSR-LodfNet, an enhanced semantic-segmentation model driven by multi-scale remote-sensing imagery, enabling high-precision lodging mapping from regional to field scales. The study selected 13 state-owned farms in Jiansanjiang, Heilongjiang Province, and jointly used PlanetScope satellite images (3 m) and UAV images (0.2 m) to build an integrated workflow of “satellite macro-monitoring, UAV fine verification, and agronomic factor coupling analysis.” The model synergistically optimizes WFNet, DenseASPP multi-scale context enhancement, and Condensed Attention, markedly improving feature extraction and boundary recognition under multi-source imagery. Experimental results show that the model achieves mIoU 84.34% and mPA 93.31% on UAV images and mIoU 81.96% and mPA 90.63% on PlanetScope images, demonstrating excellent cross-scale adaptability and stability. Causal analysis shows that the high-EVI range is significantly positively correlated with lodging probability; its risk is about 6 times that of the low-EVI range, and the lodging probability of direct-seeded rice is about 2.56 times that of transplanted rice, indicating that it may be associated with a higher lodging risk. The results demonstrate that m...