LodgeNet: Improved rice lodging recognition using semantic segmentation of UAV high-resolution remote sensing images. Su, Z., Wang, Y., Xu, Q., Gao, R., & Kong, Q. Computers and Electronics in Agriculture, 196:106873, May, 2022. GSCC: 0000054 2026-03-09T21:28:42.099Z 0.27![LodgeNet: Improved rice lodging recognition using semantic segmentation of UAV high-resolution remote sensing images [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Rice lodging not only causes difficulty in harvest operations, but also drastically reduces yield. Therefore, it is very important to identify rice lodging efficiently. For unmanned aerial vehicle (UAV) remote sensing images, this paper combines the advantages of dense block, DenseNet, attention mechanism, and jump connection on the basis of U-Net network to propose an end-to-end, pixel-to-pixel semantic segmentation method to identify rice lodging. And the method can process the input multi-band image. The accuracy of the model proposed in this paper was 97.30% on rice lodging images, which performed better than other comparison methods in the test. At the same time, it has good effect on small sample data set. The results show that it is feasible to use the improved U-Net network model to extract the lodging area of rice, which provide a useful reference for rice breeding and agricultural insurance claims.
@article{su_lodgenet_2022,
title = {{LodgeNet}: {Improved} rice lodging recognition using semantic segmentation of {UAV} high-resolution remote sensing images},
volume = {196},
issn = {0168-1699},
shorttitle = {{LodgeNet}},
url = {https://www.sciencedirect.com/science/article/pii/S0168169922001909},
doi = {10.1016/j.compag.2022.106873},
abstract = {Rice lodging not only causes difficulty in harvest operations, but also drastically reduces yield. Therefore, it is very important to identify rice lodging efficiently. For unmanned aerial vehicle (UAV) remote sensing images, this paper combines the advantages of dense block, DenseNet, attention mechanism, and jump connection on the basis of U-Net network to propose an end-to-end, pixel-to-pixel semantic segmentation method to identify rice lodging. And the method can process the input multi-band image. The accuracy of the model proposed in this paper was 97.30\% on rice lodging images, which performed better than other comparison methods in the test. At the same time, it has good effect on small sample data set. The results show that it is feasible to use the improved U-Net network model to extract the lodging area of rice, which provide a useful reference for rice breeding and agricultural insurance claims.},
language = {en},
urldate = {2026-03-09},
journal = {Computers and Electronics in Agriculture},
author = {Su, Zhongbin and Wang, Yue and Xu, Qi and Gao, Rui and Kong, Qingming},
month = may,
year = {2022},
note = {GSCC: 0000054 2026-03-09T21:28:42.099Z 0.27},
keywords = {Deep learning, End-to-end neural network, Small sample data set, U-Net},
pages = {106873},
}
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