Quantifying physiological trait variation with automated hyperspectral imaging in rice
Advancements in hyperspectral imaging (HSI) together with the establishment of dedicated plant phenotyping facilities worldwide have enabled high-throughput collection of plant spectral images with the aim of inferring target phenotypes. Here, we test the utility of HSI-derived canopy data, which we...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-09-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1229161/full |
| _version_ | 1797679217817681920 |
|---|---|
| author | To-Chia Ting Augusto C. M. Souza Rachel K. Imel Carmela R. Guadagno Chris Hoagland Yang Yang Diane R. Wang |
| author_facet | To-Chia Ting Augusto C. M. Souza Rachel K. Imel Carmela R. Guadagno Chris Hoagland Yang Yang Diane R. Wang |
| author_sort | To-Chia Ting |
| collection | DOAJ |
| description | Advancements in hyperspectral imaging (HSI) together with the establishment of dedicated plant phenotyping facilities worldwide have enabled high-throughput collection of plant spectral images with the aim of inferring target phenotypes. Here, we test the utility of HSI-derived canopy data, which were collected as part of an automated plant phenotyping system, to predict physiological traits in cultivated Asian rice (Oryza sativa). We evaluated 23 genetically diverse rice accessions from two subpopulations under two contrasting nitrogen conditions and measured 14 leaf- and canopy-level parameters to serve as ground-reference observations. HSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88) and carbon to nitrogen ratio (C:N, n=88) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection (validation: R2 = 0.797 and RMSEP = 0.264 for N; R2 = 0.592 and RMSEP = 1.688 for C:N). Results demonstrated that models developed using training data from one rice subpopulation were able to predict N and C:N in the other subpopulation, while models trained on a single treatment group were not able to predict samples from the other treatment. Finally, optimization of PLSR-RReliefF hyperparameters showed that 300-400 wavelengths generally yielded the best model performance with a minimum calibration sample size of 62. Results support the use of canopy-level hyperspectral imaging data to estimate leaf-level N and C:N across diverse rice, and this work highlights the importance of considering calibration set design prior to data collection as well as hyperparameter optimization for model development in future studies. |
| first_indexed | 2024-03-11T23:11:18Z |
| format | Article |
| id | doaj.art-30b5ea48103c4718817636fbf59a4b44 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-03-11T23:11:18Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-30b5ea48103c4718817636fbf59a4b442023-09-21T08:04:56ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-09-011410.3389/fpls.2023.12291611229161Quantifying physiological trait variation with automated hyperspectral imaging in riceTo-Chia Ting0Augusto C. M. Souza1Rachel K. Imel2Carmela R. Guadagno3Chris Hoagland4Yang Yang5Diane R. Wang6Agronomy Department, Purdue University, West Lafayette, IN, United StatesInstitute for Plant Sciences, Purdue University, West Lafayette, IN, United StatesAgronomy Department, Purdue University, West Lafayette, IN, United StatesBotany Department, University of Wyoming, Laramie, WY, United StatesInstitute for Plant Sciences, Purdue University, West Lafayette, IN, United StatesInstitute for Plant Sciences, Purdue University, West Lafayette, IN, United StatesAgronomy Department, Purdue University, West Lafayette, IN, United StatesAdvancements in hyperspectral imaging (HSI) together with the establishment of dedicated plant phenotyping facilities worldwide have enabled high-throughput collection of plant spectral images with the aim of inferring target phenotypes. Here, we test the utility of HSI-derived canopy data, which were collected as part of an automated plant phenotyping system, to predict physiological traits in cultivated Asian rice (Oryza sativa). We evaluated 23 genetically diverse rice accessions from two subpopulations under two contrasting nitrogen conditions and measured 14 leaf- and canopy-level parameters to serve as ground-reference observations. HSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88) and carbon to nitrogen ratio (C:N, n=88) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection (validation: R2 = 0.797 and RMSEP = 0.264 for N; R2 = 0.592 and RMSEP = 1.688 for C:N). Results demonstrated that models developed using training data from one rice subpopulation were able to predict N and C:N in the other subpopulation, while models trained on a single treatment group were not able to predict samples from the other treatment. Finally, optimization of PLSR-RReliefF hyperparameters showed that 300-400 wavelengths generally yielded the best model performance with a minimum calibration sample size of 62. Results support the use of canopy-level hyperspectral imaging data to estimate leaf-level N and C:N across diverse rice, and this work highlights the importance of considering calibration set design prior to data collection as well as hyperparameter optimization for model development in future studies.https://www.frontiersin.org/articles/10.3389/fpls.2023.1229161/fullOryza sativagenetic diversitygrowth traitshigh-throughput phenotypingnitrogen |
| spellingShingle | To-Chia Ting Augusto C. M. Souza Rachel K. Imel Carmela R. Guadagno Chris Hoagland Yang Yang Diane R. Wang Quantifying physiological trait variation with automated hyperspectral imaging in rice Frontiers in Plant Science Oryza sativa genetic diversity growth traits high-throughput phenotyping nitrogen |
| title | Quantifying physiological trait variation with automated hyperspectral imaging in rice |
| title_full | Quantifying physiological trait variation with automated hyperspectral imaging in rice |
| title_fullStr | Quantifying physiological trait variation with automated hyperspectral imaging in rice |
| title_full_unstemmed | Quantifying physiological trait variation with automated hyperspectral imaging in rice |
| title_short | Quantifying physiological trait variation with automated hyperspectral imaging in rice |
| title_sort | quantifying physiological trait variation with automated hyperspectral imaging in rice |
| topic | Oryza sativa genetic diversity growth traits high-throughput phenotyping nitrogen |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1229161/full |
| work_keys_str_mv | AT tochiating quantifyingphysiologicaltraitvariationwithautomatedhyperspectralimaginginrice AT augustocmsouza quantifyingphysiologicaltraitvariationwithautomatedhyperspectralimaginginrice AT rachelkimel quantifyingphysiologicaltraitvariationwithautomatedhyperspectralimaginginrice AT carmelarguadagno quantifyingphysiologicaltraitvariationwithautomatedhyperspectralimaginginrice AT chrishoagland quantifyingphysiologicaltraitvariationwithautomatedhyperspectralimaginginrice AT yangyang quantifyingphysiologicaltraitvariationwithautomatedhyperspectralimaginginrice AT dianerwang quantifyingphysiologicaltraitvariationwithautomatedhyperspectralimaginginrice |