Assessment of sunflower water stress using infrared thermometry and computer vision analysis

The objectives of the current study were to implement affordable and non-invasive measurements of infrared thermometry, leaf relative water content (RWC), crop water stress index (CWSI), leaf area index (LAI) from computer vision analysis and seed yield of sunflowers. The experiment was designed as...

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Autores principales:	Atefeh Nouraki, Samira Akhavan, Yousef Rezaei, Sigfredo Fuentes
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	crop water stress index digital image processing leaf area index seed yield vegetation indices Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/15b8502af6ed4bb9aec3d68e0208f79c
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spelling	oai:doaj.org-article:15b8502af6ed4bb9aec3d68e0208f79c2021-11-06T07:12:44ZAssessment of sunflower water stress using infrared thermometry and computer vision analysis1606-97491607-079810.2166/ws.2020.382https://doaj.org/article/15b8502af6ed4bb9aec3d68e0208f79c2021-05-01T00:00:00Zhttp://ws.iwaponline.com/content/21/3/1228https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798The objectives of the current study were to implement affordable and non-invasive measurements of infrared thermometry, leaf relative water content (RWC), crop water stress index (CWSI), leaf area index (LAI) from computer vision analysis and seed yield of sunflowers. The experiment was designed as split-plot based on randomized complete blocks with three replications. Treatments were four different levels of deficit irrigation as the main plots and three fertilization treatments were applied as sub-plots. Results showed a significant effect (P ≤ 0.01) of water stress and fertilizer on CWSI during different stages of sunflower growth. Changes in fertilizer amount and type resulted in a change in lower (dTLL) and upper (dTUL) limits of canopy-air temperature difference. A combination of chemical fertilizer with biofertilizer could help to decrease CWSI. From computer vision analysis, the normalized difference red blue index (NDRBI) had a strong linear relationship with RWC and CWSI for sunflowers (R2 of 0.87 and 0.93, respectively) and the normalized difference green blue index (NDGBI) had a linear relationship with seed yield (R2 = 0.79). Therefore, analysis of digital RGB images and CWSI were efficient, non-destructive and low-cost methods to assess crop water status for sunflowers under different irrigation and fertilizer treatments. HIGHLIGHTS The CWSI values were sensitive not only to different irrigation regimes but also to amount and type of fertilizer.; The CWSI can be derived from the plant index (NDRBI) and used for appropriate irrigation scheduling.; A positive correlation was observed between image indices with CWSI and LAI.; Combination of chemical fertilizer with biofertilizer could help to decrease CWSI.;Atefeh NourakiSamira AkhavanYousef RezaeiSigfredo FuentesIWA Publishingarticlecrop water stress indexdigital image processingleaf area indexseed yieldvegetation indicesWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 3, Pp 1228-1242 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	crop water stress index digital image processing leaf area index seed yield vegetation indices Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	crop water stress index digital image processing leaf area index seed yield vegetation indices Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Atefeh Nouraki Samira Akhavan Yousef Rezaei Sigfredo Fuentes Assessment of sunflower water stress using infrared thermometry and computer vision analysis
description	The objectives of the current study were to implement affordable and non-invasive measurements of infrared thermometry, leaf relative water content (RWC), crop water stress index (CWSI), leaf area index (LAI) from computer vision analysis and seed yield of sunflowers. The experiment was designed as split-plot based on randomized complete blocks with three replications. Treatments were four different levels of deficit irrigation as the main plots and three fertilization treatments were applied as sub-plots. Results showed a significant effect (P ≤ 0.01) of water stress and fertilizer on CWSI during different stages of sunflower growth. Changes in fertilizer amount and type resulted in a change in lower (dTLL) and upper (dTUL) limits of canopy-air temperature difference. A combination of chemical fertilizer with biofertilizer could help to decrease CWSI. From computer vision analysis, the normalized difference red blue index (NDRBI) had a strong linear relationship with RWC and CWSI for sunflowers (R2 of 0.87 and 0.93, respectively) and the normalized difference green blue index (NDGBI) had a linear relationship with seed yield (R2 = 0.79). Therefore, analysis of digital RGB images and CWSI were efficient, non-destructive and low-cost methods to assess crop water status for sunflowers under different irrigation and fertilizer treatments. HIGHLIGHTS The CWSI values were sensitive not only to different irrigation regimes but also to amount and type of fertilizer.; The CWSI can be derived from the plant index (NDRBI) and used for appropriate irrigation scheduling.; A positive correlation was observed between image indices with CWSI and LAI.; Combination of chemical fertilizer with biofertilizer could help to decrease CWSI.;
format	article
author	Atefeh Nouraki Samira Akhavan Yousef Rezaei Sigfredo Fuentes
author_facet	Atefeh Nouraki Samira Akhavan Yousef Rezaei Sigfredo Fuentes
author_sort	Atefeh Nouraki
title	Assessment of sunflower water stress using infrared thermometry and computer vision analysis
title_short	Assessment of sunflower water stress using infrared thermometry and computer vision analysis
title_full	Assessment of sunflower water stress using infrared thermometry and computer vision analysis
title_fullStr	Assessment of sunflower water stress using infrared thermometry and computer vision analysis
title_full_unstemmed	Assessment of sunflower water stress using infrared thermometry and computer vision analysis
title_sort	assessment of sunflower water stress using infrared thermometry and computer vision analysis
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/15b8502af6ed4bb9aec3d68e0208f79c
work_keys_str_mv	AT atefehnouraki assessmentofsunflowerwaterstressusinginfraredthermometryandcomputervisionanalysis AT samiraakhavan assessmentofsunflowerwaterstressusinginfraredthermometryandcomputervisionanalysis AT yousefrezaei assessmentofsunflowerwaterstressusinginfraredthermometryandcomputervisionanalysis AT sigfredofuentes assessmentofsunflowerwaterstressusinginfraredthermometryandcomputervisionanalysis
_version_	1718443777879703552

Assessment of sunflower water stress using infrared thermometry and computer vision analysis

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