Unmanned Aerial Vehicle (UAV)–Based Imaging Spectroscopy for Predicting Wheat Leaf Nitrogen

Photogrammetric Engineering & Remote Sensing(2023)

引用 2|浏览17
暂无评分
摘要
Quantitative estimation of crop nitrogen is the key to site-specific management for enhanced nitrogen (N) use efficiency and a sustainable crop production system. As an alternate to the conventional approach through wet chemistry, sensor-based noninvasive, rapid, and near-real-time assessment of crop N at the field scale has been the need for precision agriculture. The present study attempts to predict leaf N of wheat crop through spectroscopy using a field portable spectroradiometer (spectral range of 400–2500 nm) on the ground in the crop field and an imaging spectrometer (spectral range of 400–1000 nm) from an unmanned aerial vehicle (UAV) with the objectives to evaluate (1) four multivariate spectral models (i.e., artificial neural network, extreme learning machine [ELM], least absolute shrinkage and selection operator, and support vector machine regression) and (2) two sets of hyperspectral data collected from two platforms and two different sensors. In the former part of the study, ELM outperforms the other methods with maximum calibration and validation R2 of 0.99 and 0.96, respectively. Furthermore, the image data set acquired from UAV gives higher performance compared to field spectral data. Also, significant bands are identified using stepwise multiple linear regression and used for modeling to generate a wheat leaf N map of the experimental field.
更多
查看译文
关键词
unmanned aerial vehicle,uav–based,wheat,nitrogen,imaging
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要