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

• Published in: Biosystems engineering 2021-02, Vol.202, p.42-54
• Main Authors: Lu, Junsheng, Cheng, Dongling, Geng, Chenming, Zhang, Zhitao, Xiang, Youzhen, Hu, Tiantian
• Format: Article
• Language: English
• Subjects: Canopy coverage (CC); Image-based point cloud; Leaf nitrogen concentration (LNC); Plant height (PH); RGB camera; Vegetation index (VI)
• ISSN: 1537-5110; 1537-5129
• DOI: 10.1016/j.biosystemseng.2020.11.010

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 (R2 = 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 (R2 = 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 (R2 = 0.758, RMSE = 0.147%). Therefore, combining crop agronomy variables and visible VIs from UAV-based RGB images possesses the potential for estimating LNC. •A low-cost UAV-based RGB imaging system was used to estimate PH, CC and LNC.•The 99.9th percentile height of point cloud has the highest correlation with PH.•Image-based point cloud method can accurately estimate CC.•Visible VIs of UAV-based RGB images have low accuracy in estimating LNC.•Combining PH, CC and visible VIs can accurately estimate summer maize LNC.