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Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light
BACKGROUND: The color of crop leaves is closely correlated with nitrogen (N) status and can be quantified easily with a digital still color camera and image processing software. The establishment of the relationship between image color indices and N status under natural light is important for crop m...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4236477/ https://www.ncbi.nlm.nih.gov/pubmed/25411579 http://dx.doi.org/10.1186/1746-4811-10-36 |
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author | Wang, Yuan Wang, Dejian Shi, Peihua Omasa, Kenji |
author_facet | Wang, Yuan Wang, Dejian Shi, Peihua Omasa, Kenji |
author_sort | Wang, Yuan |
collection | PubMed |
description | BACKGROUND: The color of crop leaves is closely correlated with nitrogen (N) status and can be quantified easily with a digital still color camera and image processing software. The establishment of the relationship between image color indices and N status under natural light is important for crop monitoring and N diagnosis in the field. In our study, a digital still color camera was used to take pictures of the canopies of 6 rice (Oryza sativa L.) cultivars with N treatments ranging from 0 to 315 kg N ha(-1) in the field under sunny and overcast conditions in 2010 and 2011, respectively. RESULTS: Significant correlations were observed between SPAD readings, leaf N concentration (LNC) and 13 image color indices calculated from digital camera images using three color models: RGB, widely used additive color model; HSV, a cylindrical-coordinate similar to the human perception of colors; and the L(*)a(*)b(*) system of the International Commission on Illumination. Among these color indices, the index b(*), which represents the visual perception of yellow-blue chroma, has the closest linear relationship with SPAD reading and LNC. However, the relationships between LNC and color indices were affected by the developmental phase. Linear regression models were used to predict LNC and SPAD from color indices and phasic development. After that, the models were validated with independent data. Generally, acceptable performance and prediction were found between the color index b(*), SPAD reading and LNC with different cultivars and sampling dates under different natural light conditions. CONCLUSIONS: Our study showed that digital color image analysis could be a simple method of assessing rice N status under natural light conditions for different cultivars and different developmental stages. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1746-4811-10-36) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4236477 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-42364772014-11-19 Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light Wang, Yuan Wang, Dejian Shi, Peihua Omasa, Kenji Plant Methods Methodology BACKGROUND: The color of crop leaves is closely correlated with nitrogen (N) status and can be quantified easily with a digital still color camera and image processing software. The establishment of the relationship between image color indices and N status under natural light is important for crop monitoring and N diagnosis in the field. In our study, a digital still color camera was used to take pictures of the canopies of 6 rice (Oryza sativa L.) cultivars with N treatments ranging from 0 to 315 kg N ha(-1) in the field under sunny and overcast conditions in 2010 and 2011, respectively. RESULTS: Significant correlations were observed between SPAD readings, leaf N concentration (LNC) and 13 image color indices calculated from digital camera images using three color models: RGB, widely used additive color model; HSV, a cylindrical-coordinate similar to the human perception of colors; and the L(*)a(*)b(*) system of the International Commission on Illumination. Among these color indices, the index b(*), which represents the visual perception of yellow-blue chroma, has the closest linear relationship with SPAD reading and LNC. However, the relationships between LNC and color indices were affected by the developmental phase. Linear regression models were used to predict LNC and SPAD from color indices and phasic development. After that, the models were validated with independent data. Generally, acceptable performance and prediction were found between the color index b(*), SPAD reading and LNC with different cultivars and sampling dates under different natural light conditions. CONCLUSIONS: Our study showed that digital color image analysis could be a simple method of assessing rice N status under natural light conditions for different cultivars and different developmental stages. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1746-4811-10-36) contains supplementary material, which is available to authorized users. BioMed Central 2014-11-06 /pmc/articles/PMC4236477/ /pubmed/25411579 http://dx.doi.org/10.1186/1746-4811-10-36 Text en © Wang et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Wang, Yuan Wang, Dejian Shi, Peihua Omasa, Kenji Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light |
title | Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light |
title_full | Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light |
title_fullStr | Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light |
title_full_unstemmed | Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light |
title_short | Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light |
title_sort | estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4236477/ https://www.ncbi.nlm.nih.gov/pubmed/25411579 http://dx.doi.org/10.1186/1746-4811-10-36 |
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