Combining plant height, canopy coverage and vegetation index from UAV-based RGB images to estimate leaf nitrogen concentration of summer maize

被引:61
|
作者
Lu, Junsheng [1 ,2 ]
Cheng, Dongling [1 ]
Geng, Chenming [1 ,2 ]
Zhang, Zhitao [1 ,2 ]
Xiang, Youzhen [1 ,2 ]
Hu, Tiantian [1 ,2 ]
机构
[1] Northwest A&F Univ, Coll Water Resources & Architectural Engn, Yangling 712100, Shaanxi, Peoples R China
[2] Northwest A&F Univ, Minist Educ, Key Lab Agr Soil & Water Engn Arid & Semiarid Are, Yangling 712100, Shaanxi, Peoples R China
关键词
Image-based point cloud; RGB camera; Plant height (PH); Canopy coverage (CC); Leaf nitrogen concentration (LNC); Vegetation index (VI); UNMANNED AERIAL VEHICLE; CROP SURFACE MODELS; REMOTE ESTIMATION; FERTILIZER; BIOMASS; MANAGEMENT; WATER; SENSORS; SYSTEMS; NITRATE;
D O I
10.1016/j.biosystemseng.2020.11.010
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
Rapid and accurate monitoring of crop plant height (PH), canopy coverage (CC), and leaf nitrogen concentration (LNC) is essential for precision management of irrigation and fertilisation. The objectives of this study were to estimate summer maize PH by selecting optimal percentile height of point cloud; extract CC from images by using point cloud method; and determine if the combination of PH and CC with visible vegetation index (VI) could improve estimation accuracy of LNC. Images of maize field with three irrigation and four nitrogen fertiliser levels were captured using an unmanned aerial vehicle (UAV) platform with an RGB camera at summer maize grain filling stage in 2018, 2019 and 2020. The result showed that the 99.9th percentile height of point cloud was optimal for PH estimation. Image-based point cloud method could accurately estimate CC. Normalised redness intensity (NRI) had a potential for estimating LNC (R-2 = 0.474) compared with the green red ratio VI, green red VI, and atmospherically resistant VI. The relationships between four integrated VIs (PH, CC and NRI combination of two or three: NRICC, NRIH, CC*H and NRICCH) and LNC were established based on gathered dataset of 2018 and 2019, and NRICCH exhibited the highest correlation with maize LNC (R-2 1= 0.716). An independent dataset from 2020 was used to evaluate the feasibility of LNC estimation model. The result showed that the model could accurately estimate LNC (R-2 = 0.758, RMSE = 0.147%). Therefore, combining crop agronomy variables and visible VIs from UAV-based RGB images possesses the potential for estimating LNC. (c) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:42 / 54
页数:13
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