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 relative water content of sorghum and corn (R² = 0.809), as well as for nitrogen content of sorghum and corn (R² = 0.637). Reflectances at 506, 535, 583, 627, 652, 694, 722 and 964 nm were responsive to the changes in relative water content while the reflectances at 486, 521, 625, 680, 699 and 754 nm were responsive to changes in nitrogen content. High-throughput hyperspectral imaging can be used to predict physiological status of plants across genotypes and some similar species with acceptable accuracy.

Researchers should cite this work as follows:

• Lin, M.; Tuinstra, M. R. (2022). Multi-Species Prediction of Physiological Traits with Hyper-Spectral Modeling. Purdue University Research Repository. doi:10.4231/FPHP-0153

  BibTex | EndNote

1. Abiotic stress
2. Agronomy
3. Agronomy (Dept)
4. Corn
5. Ecophysiology
6. High-throughput Phenotyping
7. Machine Learning
8. nitrogen content
9. partial least square regression
10. relative water content
11. Remote Sensing
12. Sorghum