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Combining multispectral and hyperspectral data to estimate nitrogen status of tea plants (Camellia sinensis (L.) O. Kuntze) under field conditions

文献类型： 外文期刊

作者： Cao, Qiong 1 ; Yang, Guijun 2 ; Duan, Dandan 2 ; Chen, Longyue 2 ; Wang, Fan 2 ; Xu, Bo 2 ; Zhao, Chunjiang 1 ; Niu, Fanfan 2 ;

作者机构： 1.Hunan Agr Univ, Coll Mech & Elect Engn, Changsha 410125, Hunan, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 10097, Peoples R China

3.Nongxin Technol Guangzhou Co Ltd, Guangzhou 511466, Peoples R China

4.Qingyuan Smart Agr & Rural Res Inst, Qingyuan 511500, Peoples R China

5.Shaoguan Nongxin Technol Co Ltd, Guangzhou 512000, Peoples R China

关键词： Multispectral imaging; Hyperspectral; Nitrogen; VCPA

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 198 卷

页码：

收录情况： SCI

摘要： Nitrogen (N) plays a pivotal role in management of tea plantation, with significant impacts on the growth, productivity, and nutrition status of tea plants. The existing methods for N content monitoring of tea leaves are complicated and can not realize in suite and in real time way. This study proposed a method for estimating the N content of tea plants in field conditions based on a combination of a multispectral imaging system and hyperspectral data. A total of 32 parameters were extracted from five tea gardens using calibrated multispectral images of the tea plant canopy, and 27 indices were selected by Pearson correlation analysis. A total of 28 wavelengths selected by competitive adaptive reweighted sampling from hyperspectral data were combined with 27 multispectral indices as the original data. Subsequently, five variables of fused data (H, VOG, BGI, 1664 nm and 1665 nm) were selected by variable combination population analysis based on the 55 combination parameters. Partial least squares regression, random forest regression, and support vector machine regression (SVR) models all showed excellent performance for both the calibration and prediction sets. The overall results indicated that the infused data of multispectral and hyperspectral data combined with SVR are effective in monitoring the N level under field conditions, and the R-2 (coefficient of determination) and root mean square error values of the prediction were 0.9186 and 0.0560, respectively. The findings of this study are important in retaining the nutritional and quality attributes of agricultural commodities.

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