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Effects of the Spatial Resolution of UAV Images on the Prediction and Transferability of Nitrogen Content Model for Winter Wheat

文献类型： 外文期刊

作者： Guo, Yan 1 ; He, Jia 1 ; Huang, Jingyi 4 ; Jing, Yuhang 1 ; Xu, Shaobo 1 ; Wang, Laigang 1 ; Li, Shimin 1 ; Zheng, Guoqing 1 ;

作者机构： 1.Henan Acad Agr Sci, Inst Agr Econ & Informat, Zhengzhou 450002, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Huang Huai Hai Smart Agr Technol, Zhengzhou 450002, Peoples R China

3.Henan Engn Lab Crop Planting Monitoring & Warning, Zhengzhou 450002, Peoples R China

4.Univ Wisconsin Madison, Dept Soil Sci, Madison, WI 53706 USA

5.Agron Coll Henan Agr, Univ State Key Lab Wheat & Maize Crop Sci, Zhengzhou 450046, Peoples R China

关键词： UAV; spectral features; texture features; nitrogen content; random forest; model transferability

期刊名称：DRONES （ 影响因子：5.532； 五年影响因子：5.532 ）

ISSN：

年卷期： 2022 年 6 卷 10 期

页码：

收录情况： SCI

摘要： UAV imaging provides an efficient and non-destructive tool for characterizing farm information, but the quality of the UAV model is often affected by the image's spatial resolution. In this paper, the predictability of models established using UAV multispectral images with different spatial resolutions for nitrogen content of winter wheat was evaluated during the critical growth stages of winter wheat over the period 2021-2022. Feature selection based on UAV image reflectance, vegetation indices, and texture was conducted using the competitive adaptive reweighted sampling, and the random forest machine learning method was used to construct the prediction model with the optimized features. Results showed that model performance increased with decreasing image spatial resolution with a R-2, a RMSE, a MAE and a RPD of 0.84, 4.57 g m(-2), 2.50 g m(-2) and 2.34 (0.01 m spatial resolution image), 0.86, 4.15 g m(-2), 2.82 g m(-2) and 2.65 (0.02 m), and 0.92, 3.17 g m(-2), 2.45 g m(-2) and 2.86 (0.05 m), respectively. Further, the transferability of models differed when applied to images with coarser (upscaling) or finer (downscaling) resolutions. For upscaling, the model established with the 0.01 m images had a R-2 of 0.84 and 0.89 when applied to images with 0.02 m and 0.05 m resolutions, respectively. For downscaling, the model established with the 0.05 m image features had a R-2 of 0.86 and 0.83 when applied to images of 0.01 m and 0.02 m resolutions. Though the image spatial resolution affects image texture features more than the spectral features and the effects of image spatial resolution on model performance and transferability decrease with increasing plant wetness under irrigation treatment, it can be concluded that all the UAV images acquired in this study with different resolutions could achieve good predictions and transferability of the nitrogen content of winter wheat plants.

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