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Development of an accurate low cost NDVI imaging system for assessing plant health

BACKGROUND: Spectral imaging is a key method for high throughput phenotyping that can be related to a large variety of biological parameters. The Normalised Difference Vegetation Index (NDVI), uses specific wavelengths to compare crop health and performance. Increasing the accessibility of spectral...

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Autores principales: Stamford, John D., Vialet-Chabrand, Silvere, Cameron, Iain, Lawson, Tracy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9887843/
https://www.ncbi.nlm.nih.gov/pubmed/36717879
http://dx.doi.org/10.1186/s13007-023-00981-8
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author Stamford, John D.
Vialet-Chabrand, Silvere
Cameron, Iain
Lawson, Tracy
author_facet Stamford, John D.
Vialet-Chabrand, Silvere
Cameron, Iain
Lawson, Tracy
author_sort Stamford, John D.
collection PubMed
description BACKGROUND: Spectral imaging is a key method for high throughput phenotyping that can be related to a large variety of biological parameters. The Normalised Difference Vegetation Index (NDVI), uses specific wavelengths to compare crop health and performance. Increasing the accessibility of spectral imaging systems through the development of small, low cost, and easy to use platforms will generalise its use for precision agriculture. We describe a method for using a dual camera system connected to a Raspberry Pi to produce NDVI imagery, referred to as NDVIpi. Spectral reference targets were used to calibrate images into values of reflectance, that are then used to calculated NDVI with improved accuracy compared with systems that use single references/standards. RESULTS: NDVIpi imagery showed strong performance against standard spectrometry, as an accurate measurement of leaf NDVI. The NDVIpi was also compared to a relatively more expensive commercial camera (Micasense RedEdge), with both cameras having a comparable performance in measuring NDVI. There were differences between the NDVI values of the NDVIpi and the RedEdge, which could be attributed to the measurement of different wavelengths for use in the NDVI calculation by each camera. Subsequently, the wavelengths used by the NDVIpi show greater sensitivity to changes in chlorophyll content than the RedEdge. CONCLUSION: We present a methodology for a Raspberry Pi based NDVI imaging system that utilizes low cost, off-the-shelf components, and a robust multi-reference calibration protocols that provides accurate NDVI measurements. When compared with a commercial system, comparable NDVI values were obtained, despite the fact that our system was a fraction of the cost. Our results also highlight the importance of the choice of red wavelengths in the calculation of NDVI, which resulted in differences in sensitivity between camera systems. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13007-023-00981-8.
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spelling pubmed-98878432023-02-01 Development of an accurate low cost NDVI imaging system for assessing plant health Stamford, John D. Vialet-Chabrand, Silvere Cameron, Iain Lawson, Tracy Plant Methods Methodology BACKGROUND: Spectral imaging is a key method for high throughput phenotyping that can be related to a large variety of biological parameters. The Normalised Difference Vegetation Index (NDVI), uses specific wavelengths to compare crop health and performance. Increasing the accessibility of spectral imaging systems through the development of small, low cost, and easy to use platforms will generalise its use for precision agriculture. We describe a method for using a dual camera system connected to a Raspberry Pi to produce NDVI imagery, referred to as NDVIpi. Spectral reference targets were used to calibrate images into values of reflectance, that are then used to calculated NDVI with improved accuracy compared with systems that use single references/standards. RESULTS: NDVIpi imagery showed strong performance against standard spectrometry, as an accurate measurement of leaf NDVI. The NDVIpi was also compared to a relatively more expensive commercial camera (Micasense RedEdge), with both cameras having a comparable performance in measuring NDVI. There were differences between the NDVI values of the NDVIpi and the RedEdge, which could be attributed to the measurement of different wavelengths for use in the NDVI calculation by each camera. Subsequently, the wavelengths used by the NDVIpi show greater sensitivity to changes in chlorophyll content than the RedEdge. CONCLUSION: We present a methodology for a Raspberry Pi based NDVI imaging system that utilizes low cost, off-the-shelf components, and a robust multi-reference calibration protocols that provides accurate NDVI measurements. When compared with a commercial system, comparable NDVI values were obtained, despite the fact that our system was a fraction of the cost. Our results also highlight the importance of the choice of red wavelengths in the calculation of NDVI, which resulted in differences in sensitivity between camera systems. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13007-023-00981-8. BioMed Central 2023-01-30 /pmc/articles/PMC9887843/ /pubmed/36717879 http://dx.doi.org/10.1186/s13007-023-00981-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Methodology
Stamford, John D.
Vialet-Chabrand, Silvere
Cameron, Iain
Lawson, Tracy
Development of an accurate low cost NDVI imaging system for assessing plant health
title Development of an accurate low cost NDVI imaging system for assessing plant health
title_full Development of an accurate low cost NDVI imaging system for assessing plant health
title_fullStr Development of an accurate low cost NDVI imaging system for assessing plant health
title_full_unstemmed Development of an accurate low cost NDVI imaging system for assessing plant health
title_short Development of an accurate low cost NDVI imaging system for assessing plant health
title_sort development of an accurate low cost ndvi imaging system for assessing plant health
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9887843/
https://www.ncbi.nlm.nih.gov/pubmed/36717879
http://dx.doi.org/10.1186/s13007-023-00981-8
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