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A hyperspectral plant health monitoring system for space crop production
Compact and automated sensing systems are needed to monitor plant health for NASA’s controlled-environment space crop production. A new hyperspectral system was designed for early detection of plant stresses using both reflectance and fluorescence imaging in visible and near-infrared (VNIR) waveleng...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352677/ https://www.ncbi.nlm.nih.gov/pubmed/37469773 http://dx.doi.org/10.3389/fpls.2023.1133505 |
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author | Qin, Jianwei Monje, Oscar Nugent, Matthew R. Finn, Joshua R. O’Rourke, Aubrie E. Wilson, Kristine D. Fritsche, Ralph F. Baek, Insuck Chan, Diane E. Kim, Moon S. |
author_facet | Qin, Jianwei Monje, Oscar Nugent, Matthew R. Finn, Joshua R. O’Rourke, Aubrie E. Wilson, Kristine D. Fritsche, Ralph F. Baek, Insuck Chan, Diane E. Kim, Moon S. |
author_sort | Qin, Jianwei |
collection | PubMed |
description | Compact and automated sensing systems are needed to monitor plant health for NASA’s controlled-environment space crop production. A new hyperspectral system was designed for early detection of plant stresses using both reflectance and fluorescence imaging in visible and near-infrared (VNIR) wavelength range (400–1000 nm). The prototype system mainly includes two LED line lights providing VNIR broadband and UV-A (365 nm) light for reflectance and fluorescence measurement, respectively, a line-scan hyperspectral camera, and a linear motorized stage with a travel range of 80 cm. In an overhead sensor-to-sample arrangement, the stage translates the lights and camera over the plants to acquire reflectance and fluorescence images in sequence during one cycle of line-scan imaging. System software was developed using LabVIEW to realize hardware parameterization, data transfer, and automated imaging functions. The imaging unit was installed in a plant growth chamber at NASA Kennedy Space Center for health monitoring studies for pick-and-eat salad crops. A preliminary experiment was conducted to detect plant drought stress for twelve Dragoon lettuce samples, of which half were well-watered and half were under-watered while growing. A machine learning method using an optimized discriminant classifier based on VNIR reflectance spectra generated classification accuracies over 90% for the first four days of the stress treatment, showing great potential for early detection of the drought stress on lettuce leaves before any visible symptoms and size differences were evident. The system is promising to provide useful information for optimization of growth environment and early mitigation of stresses in space crop production. |
format | Online Article Text |
id | pubmed-10352677 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103526772023-07-19 A hyperspectral plant health monitoring system for space crop production Qin, Jianwei Monje, Oscar Nugent, Matthew R. Finn, Joshua R. O’Rourke, Aubrie E. Wilson, Kristine D. Fritsche, Ralph F. Baek, Insuck Chan, Diane E. Kim, Moon S. Front Plant Sci Plant Science Compact and automated sensing systems are needed to monitor plant health for NASA’s controlled-environment space crop production. A new hyperspectral system was designed for early detection of plant stresses using both reflectance and fluorescence imaging in visible and near-infrared (VNIR) wavelength range (400–1000 nm). The prototype system mainly includes two LED line lights providing VNIR broadband and UV-A (365 nm) light for reflectance and fluorescence measurement, respectively, a line-scan hyperspectral camera, and a linear motorized stage with a travel range of 80 cm. In an overhead sensor-to-sample arrangement, the stage translates the lights and camera over the plants to acquire reflectance and fluorescence images in sequence during one cycle of line-scan imaging. System software was developed using LabVIEW to realize hardware parameterization, data transfer, and automated imaging functions. The imaging unit was installed in a plant growth chamber at NASA Kennedy Space Center for health monitoring studies for pick-and-eat salad crops. A preliminary experiment was conducted to detect plant drought stress for twelve Dragoon lettuce samples, of which half were well-watered and half were under-watered while growing. A machine learning method using an optimized discriminant classifier based on VNIR reflectance spectra generated classification accuracies over 90% for the first four days of the stress treatment, showing great potential for early detection of the drought stress on lettuce leaves before any visible symptoms and size differences were evident. The system is promising to provide useful information for optimization of growth environment and early mitigation of stresses in space crop production. Frontiers Media S.A. 2023-07-04 /pmc/articles/PMC10352677/ /pubmed/37469773 http://dx.doi.org/10.3389/fpls.2023.1133505 Text en Copyright © 2023 Qin, Monje, Nugent, Finn, O’Rourke, Wilson, Fritsche, Baek, Chan and Kim https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Qin, Jianwei Monje, Oscar Nugent, Matthew R. Finn, Joshua R. O’Rourke, Aubrie E. Wilson, Kristine D. Fritsche, Ralph F. Baek, Insuck Chan, Diane E. Kim, Moon S. A hyperspectral plant health monitoring system for space crop production |
title | A hyperspectral plant health monitoring system for space crop production |
title_full | A hyperspectral plant health monitoring system for space crop production |
title_fullStr | A hyperspectral plant health monitoring system for space crop production |
title_full_unstemmed | A hyperspectral plant health monitoring system for space crop production |
title_short | A hyperspectral plant health monitoring system for space crop production |
title_sort | hyperspectral plant health monitoring system for space crop production |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352677/ https://www.ncbi.nlm.nih.gov/pubmed/37469773 http://dx.doi.org/10.3389/fpls.2023.1133505 |
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