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Estimation of canopy nitrogen nutrient status in lodging maize using unmanned aerial vehicles hyperspectral data

文献类型： 外文期刊

作者： Sun, Qian 1 ; Chen, Liping 1 ; Gu, Xiaohe 2 ; Zhang, Sen 2 ; Dai, Menglei 2 ; Zhou, Jingping 2 ; Gu, Limin 4 ; Zhen, Wenchao 4 ;

作者机构： 1.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100097, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing 100097, Peoples R China

4.Minist Agr & Rural Affairs, Key Lab North China Water Saving Agr, Baoding 071001, Hebei, Peoples R China

5.Hebei Agr Univ, Coll Agron, Baoding 071001, Hebei, Peoples R China

关键词： Lodging stress; Maize; Canopy hyperspectrum; Canopy nitrogen concentration (CNC); Canopy nitrogen volumetric density (CNVD)

期刊名称：ECOLOGICAL INFORMATICS （ 影响因子：5.1； 五年影响因子：4.9 ）

ISSN： 1574-9541

年卷期： 2023 年 78 卷

页码：

收录情况： SCI

摘要： Rapid and non-destructive monitoring of the temporal dynamic changes in canopy nitrogen in lodging maize is essential to explore the influence on plant nutrient transport and yield loss. This study aims to monitor the canopy nitrogen status and lodging severity in maize using unmanned aerial vehicle (UAV) hyperspectral technology. The lodging maize experiments, involving different types, were conducted at the vegetative tasseling (VT) and reproductive milk (R3) stages. Agronomic traits and hyperspectral images were collected at 1, 7, 14, 21 and 28 days after lodging (DAL). Lodging intensity was quantified using canopy nitrogen concentration (CNC) and canopy nitrogen volumetric density (CNVD). Firstly, the variation in CNC and CNVD among different lodging types was analyzed, and the correlation between CNC, CNVD and the canopy hyperspectrum were assessed. Subsequently, the recursive feature elimination with cross-validation (RFECV) algorithm was used to screen wavelengths sensitive to CNC and CNVD. Next, the CNC and CNVD estimation models were developed using random forest regression (RFR) and gradient boosting regression (GBR) algorithms. Finally, the spatial distribution maps of CNC and CNVD in lodging maize were generated based on UAV images. The key findings were as follows: (1) CNVD exhibited more significant variation than CNC in different lodging types, with greater lodging severity corresponding to higher CNVD values; (2) the correlation between CNVD and canopy hyperspectrum was stronger than that of CNC; (3) there were 12 CNC-sensitive and 16 CNVD-sensitive wavelengths selected by RFECV algorithm; (4) the CNVD estimation model performed the best (R2 = 0.77, RMSE = 69.61 g/m3 in testing set) using the GBR algorithm. Therefore, the combination of feature selection with regression models effectively reduces hyperspectral data dimensionality. This enhances the estimation accuracy and computational efficiency for assessing canopy nitrogen nutrient status in lodging maize.

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