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Classification of Plant Endogenous States Using Machine Learning-Derived Agricultural Indices

Leaf color patterns vary depending on leaf age, pathogen infection, and environmental and nutritional stresses; thus, they are widely used to diagnose plant health statuses in agricultural fields. The visible-near infrared-shortwave infrared (VIS-NIR-SWIR) sensor measures the leaf color pattern from...

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Autores principales: Koh, Sally Shuxian, Dev, Kapil, Tan, Javier Jingheng, Teo, Valerie Xinhui, Zhang, Shuyan, U.S., Dinish, Olivo, Malini, Urano, Daisuke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AAAS 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10298216/
https://www.ncbi.nlm.nih.gov/pubmed/37383729
http://dx.doi.org/10.34133/plantphenomics.0060
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author Koh, Sally Shuxian
Dev, Kapil
Tan, Javier Jingheng
Teo, Valerie Xinhui
Zhang, Shuyan
U.S., Dinish
Olivo, Malini
Urano, Daisuke
author_facet Koh, Sally Shuxian
Dev, Kapil
Tan, Javier Jingheng
Teo, Valerie Xinhui
Zhang, Shuyan
U.S., Dinish
Olivo, Malini
Urano, Daisuke
author_sort Koh, Sally Shuxian
collection PubMed
description Leaf color patterns vary depending on leaf age, pathogen infection, and environmental and nutritional stresses; thus, they are widely used to diagnose plant health statuses in agricultural fields. The visible-near infrared-shortwave infrared (VIS-NIR-SWIR) sensor measures the leaf color pattern from a wide spectral range with high spectral resolution. However, spectral information has only been employed to understand general plant health statuses (e.g., vegetation index) or phytopigment contents, rather than pinpointing defects of specific metabolic or signaling pathways in plants. Here, we report feature engineering and machine learning methods that utilize VIS-NIR-SWIR leaf reflectance for robust plant health diagnostics, pinpointing physiological alterations associated with the stress hormone, abscisic acid (ABA). Leaf reflectance spectra of wild-type, ABA2-overexpression, and deficient plants were collected under watered and drought conditions. Drought- and ABA-associated normalized reflectance indices (NRIs) were screened from all possible pairs of wavelength bands. Drought associated NRIs showed only a partial overlap with those related to ABA deficiency, but more NRIs were associated with drought due to additional spectral changes within the NIR wavelength range. Interpretable support vector machine classifiers built with 20 NRIs predicted treatment or genotype groups with an accuracy greater than those with conventional vegetation indices. Major selected NRIs were independent from leaf water content and chlorophyll content, 2 well-characterized physiological changes under drought. The screening of NRIs, streamlined with the development of simple classifiers, serves as the most efficient means of detecting reflectance bands that are highly relevant to characteristics of interest.
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spelling pubmed-102982162023-06-28 Classification of Plant Endogenous States Using Machine Learning-Derived Agricultural Indices Koh, Sally Shuxian Dev, Kapil Tan, Javier Jingheng Teo, Valerie Xinhui Zhang, Shuyan U.S., Dinish Olivo, Malini Urano, Daisuke Plant Phenomics Research Article Leaf color patterns vary depending on leaf age, pathogen infection, and environmental and nutritional stresses; thus, they are widely used to diagnose plant health statuses in agricultural fields. The visible-near infrared-shortwave infrared (VIS-NIR-SWIR) sensor measures the leaf color pattern from a wide spectral range with high spectral resolution. However, spectral information has only been employed to understand general plant health statuses (e.g., vegetation index) or phytopigment contents, rather than pinpointing defects of specific metabolic or signaling pathways in plants. Here, we report feature engineering and machine learning methods that utilize VIS-NIR-SWIR leaf reflectance for robust plant health diagnostics, pinpointing physiological alterations associated with the stress hormone, abscisic acid (ABA). Leaf reflectance spectra of wild-type, ABA2-overexpression, and deficient plants were collected under watered and drought conditions. Drought- and ABA-associated normalized reflectance indices (NRIs) were screened from all possible pairs of wavelength bands. Drought associated NRIs showed only a partial overlap with those related to ABA deficiency, but more NRIs were associated with drought due to additional spectral changes within the NIR wavelength range. Interpretable support vector machine classifiers built with 20 NRIs predicted treatment or genotype groups with an accuracy greater than those with conventional vegetation indices. Major selected NRIs were independent from leaf water content and chlorophyll content, 2 well-characterized physiological changes under drought. The screening of NRIs, streamlined with the development of simple classifiers, serves as the most efficient means of detecting reflectance bands that are highly relevant to characteristics of interest. AAAS 2023-06-27 /pmc/articles/PMC10298216/ /pubmed/37383729 http://dx.doi.org/10.34133/plantphenomics.0060 Text en Copyright © 2023 Sally Shuxian Koh et al. https://creativecommons.org/licenses/by/4.0/Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Article
Koh, Sally Shuxian
Dev, Kapil
Tan, Javier Jingheng
Teo, Valerie Xinhui
Zhang, Shuyan
U.S., Dinish
Olivo, Malini
Urano, Daisuke
Classification of Plant Endogenous States Using Machine Learning-Derived Agricultural Indices
title Classification of Plant Endogenous States Using Machine Learning-Derived Agricultural Indices
title_full Classification of Plant Endogenous States Using Machine Learning-Derived Agricultural Indices
title_fullStr Classification of Plant Endogenous States Using Machine Learning-Derived Agricultural Indices
title_full_unstemmed Classification of Plant Endogenous States Using Machine Learning-Derived Agricultural Indices
title_short Classification of Plant Endogenous States Using Machine Learning-Derived Agricultural Indices
title_sort classification of plant endogenous states using machine learning-derived agricultural indices
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10298216/
https://www.ncbi.nlm.nih.gov/pubmed/37383729
http://dx.doi.org/10.34133/plantphenomics.0060
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