Lin Meng-Yang, Lynch Valerie, Ma Dongdong, Maki Hideki, Jin Jian, Tuinstra Mitchell
Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA.
Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA.
Plants (Basel). 2022 Mar 1;11(5):676. doi: 10.3390/plants11050676.
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 ( (L.) Moench) and six genotypes of corn ( L.) under varying water and nitrogen treatments. Multi-species models were predictive for the relative water content of sorghum and corn (R = 0.809), as well as for the nitrogen content of sorghum and corn (R = 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.
高通量表型分析的缺乏是作物非生物胁迫耐受性育种的一个瓶颈。高效且无损的高光谱成像能够量化非生物胁迫下的植物生理性状；然而，预测模型通常是针对一个物种的少数基因型开发的，这限制了该技术的广泛应用。因此，本研究的目的是探索通过偏最小二乘回归，基于高光谱反射率为三种高粱基因型（(L.) Moench）和六种玉米基因型（L.）在不同水分和氮素处理下开发跨物种模型来预测生理性状（相对含水量和氮含量）的可能性。多物种模型对高粱和玉米的相对含水量（R = 0.809）以及高粱和玉米的氮含量（R = 0.637）具有预测性。506、535、583、627、652、694、722和964 nm处的反射率对相对含水量的变化有响应，而486、521、625、680、699和754 nm处的反射率对氮含量的变化有响应。高通量高光谱成像可用于以可接受的准确度预测不同基因型以及一些相似物种的植物生理状态。
