Multi-Species Prediction of Physiological Traits with Hyperspectral Modeling.

Abstract

Lack of high-throughput phenotyping is a bottleneck to breeding for abiotic stress tolerance in crop plants. Efficient and non-destructive hyperspectral imaging can quantify plant physiological traits under abiotic stresses; however, prediction models generally are developed for few genotypes of one species, limiting the broader applications of this technology. Therefore, the objective of this research was to explore the possibility of developing cross-species models to predict physiological traits (relative water content and nitrogen content) based on hyperspectral reflectance through partial least square regression for three genotypes of sorghum (Sorghum bicolor (L.) Moench) and six genotypes of corn (Zea mays L.) under varying water and nitrogen treatments. Multi-species models were predictive for the relative water content of sorghum and corn (R2 = 0.809), as well as for the nitrogen content of sorghum and corn (R2 = 0.637). Reflectances at 506, 535, 583, 627, 652, 694, 722, and 964 nm were responsive to changes in the relative water content, while the reflectances at 486, 521, 625, 680, 699, and 754 nm were responsive to changes in the nitrogen content. High-throughput hyperspectral imaging can be used to predict physiological status of plants across genotypes and some similar species with acceptable accuracy.

Plant phenotyping relevance

ハイスループット hyperspectral imaging と回帰モデルにより、植物の相対含水量・窒素含量という生理形質を非破壊推定する手法を開発・評価しており、フェノタイピング手法が研究の中心である。

Code and data availability

The paper's hyperspectral reflectance measurements, RWC/NC ground-reference trait data, and metadata are explicitly deposited in the Purdue University Research Repository (PURR) with a public URL stated in the Data Availability Statement. The MDPI supplement contains only stepwise regression tables, model evaluation, V