A novel high-throughput hyperspectral scanner and analytical methods for predicting maize kernel composition and physical traits.

Abstract

Large-scale investigations of maize kernel traits important to researchers, breeders, and processors require high throughput methods, which are presently lacking. To address this bottleneck, we developed a novel flatbed platform that automatically acquires and analyzes multiwavelength near-infrared (NIR hyperspectral) images of maize kernels precisely enough to support robust predictions of protein content, density, and endosperm vitreousness. The upward facing-camera design and the automated ability to analyze the embryo or abgerminal sides of each individual kernel in a sample with the appropriate side-specific model helped to produce a superior combination of throughput and prediction accuracy compared to other single-kernel platforms. Protein was predicted to within 0.85% (root mean square error of prediction), density to within 0.038 g/cm 3 , and endosperm vitreousness percentage to within 6.3%. Kernel length and width were also accurately measured so that each kernel in a rapidly scanned sample was comprehensively characterized.

Plant phenotyping relevance

トウモロコシ穀粒の組成・物理形質を高スループットに取得・推定するハイパースペクトル画像プラットフォームと解析手法の開発が研究の中心である。

Code and data availability

The paper's authors explicitly state that all analysis code for the hyperspectral phenotyping pipeline (PLSR trait prediction, PLS-DA kernel-side classification, image analysis) is publicly available in their GitHub repository.