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.
Highlights
Maize has the largest yield and the widest planting area among any other crops in the world [1,2,3]
Many researches showed that SPAD value could well represent the relative value of leaf chlorophyll content, which can be used to quickly diagnose nitrogen status of crop in the field [7, 8]
The objectives of this study were to explore the feasibility of estimating main traits of maize inbred lines using unmanned aerial vehicle (UAV)-based hyperspectral images of grain filling stage, including aboveground biomass (AGB), total leaf area (TLA), SPAD value, and TWK
Summary
Maize has the largest yield and the widest planting area among any other crops in the world [1,2,3]. Total leaf area (TLA, defined as the sum of all leaf area of a single plant) and leaf chlorophyll content (LCC) are closely related to crop photosynthesis and transpiration [4,5,6]. Aboveground biomass (AGB, defined as the total amount of organic matter of plant aboveground per unit area) plays an important role in the utilization of light energy and the formation of dry matter [9, 10]. Thousand kernel weight (TWK, defined as the weight of a thousand grains) is an indicator of grain size and fullness in breeding and an important basis for field yield prediction [11, 12]. Monitoring of TLA, SPAD value, AGB, and TWK can scientifically and efficiently provide evidence for evaluating crop growth and predicting grain yields. Unmanned aerial vehicles (UAVs) provide a new way to analyze biophysical traits fast, economically, and nondestructively with high-spatial and temporal resolution images
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