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.
Highlights
Most agricultural production environments are exposed to abiotic stresses at one time or another, causing at least 50% economic loss in production each year [1]
There is increasing interest in development of high-throughput phenotyping through machine learning-integrated remote sensing [5]
Sorghum and corn, regardless of genotypes, had a significantly lower nitrogen content (NC) under the nitrogen deficit condition compared to nitrogen sufficient condition
Summary
Most agricultural production environments are exposed to abiotic stresses at one time or another, causing at least 50% economic loss in production each year [1]. Breeding for abiotic stress tolerant cultivars is essential for enhancing crop productivity and quality under global climate change [2]. Breeding progress highly depends on the efficiency and accuracy of genotyping and phenotyping [3]. Genotyping is a high-throughput process that is well adapted to different species and accurate [4]. Traditional phenotyping methods are typically time consuming, labor intensive, species specific, destructive, and expensive. There is increasing interest in development of high-throughput phenotyping through machine learning-integrated remote sensing [5]
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