High-Throughput Phenotyping for Agronomic Traits in Cassava Using Aerial Imaging.

Abstract

Large-scale phenotyping using unmanned aerial vehicles (UAVs) has been considered an important tool for plant selection. This study aimed to estimate the correlations between agronomic data and vegetation indices (VIs) obtained at different flight heights and to select prediction models to evaluate the potential use of aerial imaging in cassava breeding programs. Various VIs were obtained and analyzed using mixed models to derive the best linear unbiased predictors, heritability parameters, and correlations with various agronomic traits. The VIs were also used to build prediction models for agronomic traits. Aerial imaging showed high potential for estimating plant height, regardless of flight height (r = 0.99), although lower-altitude flights (20 m) resulted in less biased estimates of this trait. Multispectral sensors showed higher correlations compared to RGB, especially for vigor, shoot yield, and fresh root yield (−0.40 ≤ r ≤ 0.50). The heritability of VIs at different flight heights ranged from moderate to high (0.51 ≤ HCullis2 ≤ 0.94), regardless of the sensor used. The best prediction models were observed for the traits of plant vigor and dry matter content, using the Generalized Linear Model with Stepwise Feature Selection (GLMSS) and the K-Nearest Neighbor (KNN) model. The predictive ability for dry matter content increased with flight height for the GLMSS model (R2 = 0.26 at 20 m and R2 = 0.44 at 60 m), while plant vigor ranged from R2 = 0.50 at 20 m to R2 = 0.47 at 40 m in the KNN model. Our results indicate the practical potential of implementing high-throughput phenotyping via aerial imaging for rapid and efficient selection in breeding programs.

Plant phenotyping relevance

UAV画像と植生指数を用いた作物形質推定・予測モデルを開発評価し、育種向け高スループット表現型解析の実用性を検証しているため、方法が中心的である。

Code and data availability

The paper's phenotyping data and analysis outputs are said to be in the article and Supplementary Materials, with additional data available only upon request to the corresponding author. The MDPI supplementary link is public but its contents (whether it includes phenotype datasets, imagery, or code) are not described,;

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