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- Title
Identifying Corn Lodging in the Mature Period Using Chinese GF-1 PMS Images.
- Authors
Huang, Xianda; Xuan, Fu; Dong, Yi; Su, Wei; Wang, Xinsheng; Huang, Jianxi; Li, Xuecao; Zeng, Yelu; Miao, Shuangxi; Li, Jiayu
- Abstract
Efficient, fast, and accurate crop lodging monitoring is urgent for farmers, agronomists, insurance loss adjusters, and policymakers. This study aims to explore the potential of Chinese GF-1 PMS high-spatial-resolution images for corn lodging monitoring and to find a robust and efficient way to identify corn lodging accurately and efficiently. Three groups of image features and five machine-learning approaches are used for classifying non-lodged, moderately lodged, and severely lodged areas. Our results reveal that (1) the combination of spectral bands, optimized vegetation indexes, and texture features classify corn lodging with an overall accuracy of 93.81% and a Kappa coefficient of 0.91. (2) The random forest is an efficient, robust, and easy classifier to identify corn lodging with the F1-score of 0.95, 0.92, and 0.95 for non-lodged, moderately lodged, and severely lodged areas, respectively. (3) The GF-1 PMS image has great potential for identifying corn lodging on a regional scale.
- Subjects
INSURANCE adjusters; RANDOM forest algorithms; MACHINE learning; HYBRID corn
- Publication
Remote Sensing, 2023, Vol 15, Issue 4, p894
- ISSN
2072-4292
- Publication type
Article
- DOI
10.3390/rs15040894