Research Article | Open Access

Volume 2021 |Article ID 9890745 | https://doi.org/10.34133/2021/9890745

The Application of UAV-Based Hyperspectral Imaging to Estimate Crop Traits in Maize Inbred Lines

Meiyan ShuiD ,¹ Mengyuan Shen,¹ Jinyu Zuo,¹ Pengfei Yin,² Min WangiD ,² Ziwen Xie,¹ Jihua Tang,³ Ruili Wang,⁴ Baoguo Li,¹ Xiaohong YangiD ,² and Yuntao Ma ¹

¹College of Land Science and Technology, China Agricultural University, Beijing 100193, China
²State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center of China, China Agricultural University, Beijing 100193, China
³College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
⁴Agricultural Artificial Intelligence and Crop Phenotype Engineering Research Center, Inner Mongolia Institute of Biotechnology, Huhhot 010070, China

Abstract

Crop traits such as aboveground biomass (AGB), total leaf area (TLA), leaf chlorophyll content (LCC), and thousand kernel weight (TWK) are important indices in maize breeding. How to extract multiple crop traits at the same time is helpful to improve the efficiency of breeding. Compared with digital and multispectral images, the advantages of high spatial and spectral resolution of hyperspectral images derived from unmanned aerial vehicle (UAV) are expected to accurately estimate the similar traits among breeding materials. This study is aimed at exploring the feasibility of estimating AGB, TLA, SPAD value, and TWK using UAV hyperspectral images and at determining the optimal models for facilitating the process of selecting advanced varieties. The successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to screen sensitive bands for the maize traits. Partial least squares (PLS) and random forest (RF) algorithms were used to estimate the maize traits. The results can be summarized as follows: The sensitive bands for various traits were mainly concentrated in the near-red and red-edge regions. The sensitive bands screened by CARS were more abundant than those screened by SPA. For AGB, TLA, and SPAD value, the optimal combination was the CARS-PLS method. Regarding the TWK, the optimal combination was the CARS-RF method. Compared with the model built by RF, the model built by PLS was more stable. This study provides guiding significance and practical value for main trait estimation of maize inbred lines by UAV hyperspectral images at the plot level.