Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology

The pinto bean is one of widely consumed legume crop that constitutes over 42% of the U.S dry bean production. However, limited studies have been conducted in past to assess its quantitative and qualitative yield potentials. Emerging remote sensing technologies can help in such assessment. Therefore...

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Main Authors:	Rakesh Ranjan, Abhilash K. Chandel, Lav R. Khot, Haitham Y. Bahlol, Jianfeng Zhou, Rick A. Boydston, Phillip N. Miklas
Format:	Article
Language:	English
Published:	KeAi Communications Co., Ltd. 2019-12-01
Series:	Information Processing in Agriculture
Online Access:	http://www.sciencedirect.com/science/article/pii/S2214317318302312

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spelling	doaj-2604d9fbf6814dfdafd56a906c6b7efc2021-02-02T06:06:54ZengKeAi Communications Co., Ltd.Information Processing in Agriculture2214-31732019-12-0164502514Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technologyRakesh Ranjan0Abhilash K. Chandel1Lav R. Khot2Haitham Y. Bahlol3Jianfeng Zhou4Rick A. Boydston5Phillip N. Miklas6Department of Biological Systems Engineering, Washington State University, Pullman, WA, USADepartment of Biological Systems Engineering, Washington State University, Pullman, WA, USADepartment of Biological Systems Engineering, Washington State University, Pullman, WA, USA; Center for Precision and Automated Agricultural Systems, IAREC, Washington State University, Prosser, WA, USA; Corresponding author at: Department of Biological Systems Engineering, Washington State University, Pullman, WA, USA.Department of Biological Systems Engineering, Washington State University, Pullman, WA, USADepartment of Biological Systems Engineering, Washington State University, Pullman, WA, USAUSDA-ARS Grain Legume Genetics and Physiology Research Unit, Prosser, WA USAUSDA-ARS Grain Legume Genetics and Physiology Research Unit, Prosser, WA USAThe pinto bean is one of widely consumed legume crop that constitutes over 42% of the U.S dry bean production. However, limited studies have been conducted in past to assess its quantitative and qualitative yield potentials. Emerging remote sensing technologies can help in such assessment. Therefore, this study evaluates the role of ground-based multispectral imagery derived vegetation indices (VIs) for irrigated the pinto bean stress and yield assessments. Studied were eight cultivars of the pinto bean grown under conventional and strip tillage treatments and irrigated at 52% and 100% of required evapotranspiration. Imagery data was acquired using a five-band multispectral imager at early, mid and late growth stages. Commonly used 25 broadband VIs were derived to capture crop stress traits and yield potential. Principal component analysis and Spearman’s rank correlation tests were conducted to identify key VIs and their correlation (rs) with abiotic stress at each growth stage. Transformed difference vegetation index, nonlinear vegetation index (NLI), modified NLI and infrared percentage vegetation index (IPVI) were consistent in accounting the stress response and crop yield at all growth stages (rs > 0.60, coefficient of determination (R2): 0.50–0.56, P < 0.05). Ten other VIs significantly accounted for crop stress at early and late stages. Overall, identified key VIs may be helpful to growers for precise crop management decision making and breeders for crop stress response and yield assessments. Keywords: Pinto bean, Multispectral imaging, Vegetation indices, Stress assessment, Yield potentialhttp://www.sciencedirect.com/science/article/pii/S2214317318302312
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Rakesh Ranjan Abhilash K. Chandel Lav R. Khot Haitham Y. Bahlol Jianfeng Zhou Rick A. Boydston Phillip N. Miklas
spellingShingle	Rakesh Ranjan Abhilash K. Chandel Lav R. Khot Haitham Y. Bahlol Jianfeng Zhou Rick A. Boydston Phillip N. Miklas Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology Information Processing in Agriculture
author_facet	Rakesh Ranjan Abhilash K. Chandel Lav R. Khot Haitham Y. Bahlol Jianfeng Zhou Rick A. Boydston Phillip N. Miklas
author_sort	Rakesh Ranjan
title	Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology
title_short	Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology
title_full	Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology
title_fullStr	Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology
title_full_unstemmed	Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology
title_sort	irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology
publisher	KeAi Communications Co., Ltd.
series	Information Processing in Agriculture
issn	2214-3173
publishDate	2019-12-01
description	The pinto bean is one of widely consumed legume crop that constitutes over 42% of the U.S dry bean production. However, limited studies have been conducted in past to assess its quantitative and qualitative yield potentials. Emerging remote sensing technologies can help in such assessment. Therefore, this study evaluates the role of ground-based multispectral imagery derived vegetation indices (VIs) for irrigated the pinto bean stress and yield assessments. Studied were eight cultivars of the pinto bean grown under conventional and strip tillage treatments and irrigated at 52% and 100% of required evapotranspiration. Imagery data was acquired using a five-band multispectral imager at early, mid and late growth stages. Commonly used 25 broadband VIs were derived to capture crop stress traits and yield potential. Principal component analysis and Spearman’s rank correlation tests were conducted to identify key VIs and their correlation (rs) with abiotic stress at each growth stage. Transformed difference vegetation index, nonlinear vegetation index (NLI), modified NLI and infrared percentage vegetation index (IPVI) were consistent in accounting the stress response and crop yield at all growth stages (rs > 0.60, coefficient of determination (R2): 0.50–0.56, P < 0.05). Ten other VIs significantly accounted for crop stress at early and late stages. Overall, identified key VIs may be helpful to growers for precise crop management decision making and breeders for crop stress response and yield assessments. Keywords: Pinto bean, Multispectral imaging, Vegetation indices, Stress assessment, Yield potential
url	http://www.sciencedirect.com/science/article/pii/S2214317318302312
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Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology

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