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Quantifying physiological trait variation with automated hyperspectral imaging in rice
  • +4
  • To-Chia Ting,
  • Augusto Souza,
  • Rachel K Imel,
  • Carmela R Guadagno,
  • Chris Hoagland,
  • Yang Yang,
  • Diane R Wang
To-Chia Ting
Agronomy Department, Purdue University

Corresponding Author:[email protected]

Author Profile
Augusto Souza
Institute for Plant Sciences, Purdue University
Rachel K Imel
Agronomy Department, Purdue University
Carmela R Guadagno
Botany Department, University of Wyoming
Chris Hoagland
Institute for Plant Sciences, Purdue University
Yang Yang
Institute for Plant Sciences, Purdue University
Diane R Wang
Agronomy Department, Purdue University


BodyText: Hyperspectral imaging (HSI) system can facilitate the study of crop physiological responses to abiotic stress. It has been established in automated controlled-environment across the globe. Nonetheless, each crop in every new environment requires specific experimental design and data analysis pipeline. At Purdue University's Ag Alumni Phenotyping Facility (AAPF), 15 indica and eight tropical japonica rice genotypes were raised up to 13 weeks old under two nitrogen treatments. HSI data were collected two to three times per week and 14 physiological traits relating to growth, photosynthesis capacity and water transportation were measured manually. With principal component analysis (PCA), physiological trait data showed the effects of subpopulation and treatment whereas only treatment effect could be revealed in HSI data. Changes of reflectance around 715 nm (in the red edge region) were associated with the treatment effect in HSI data based on the loadings of PCA. By training support vector machine classifiers, we found that classification accuracy of treatment levels in HSI data was 80% or greater when the rice plants were six to 10 weeks old. Furthermore, leaf-level nitrogen content (N, %) and carbon to nitrogen ratio (C:N) could be predicted from HSI data by building partial least squares regression models (PLSR) with featured wavelengths. The í µí± ! values for N and C:N were 0.83 and 0.73, respectively, and normalized root mean square error of prediction for N and C:N were 13.67% and 14.39%, respectively (in validation datasets). This is the first study that showed the potential use of HSI on rice at AAPF.
23 Oct 2022Submitted to NAPPN 2023 Abstracts
28 Oct 2022Published in NAPPN 2023 Abstracts