Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data

Leaf area index (LAI) is an important variable for characterizing plant canopy in crop models. It is traditionally defined as the total one-sided leaf area per unit ground area and is estimated by both direct and indirect methods. This paper explores the effectiveness of using light detection and ra...

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Autores principales:	Behrokh Nazeri, Melba M. Crawford, Mitchell R. Tuinstra
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	high-throughput phenotyping remote sensing LiDAR leaf area index machine learning row crops Plant culture SB1-1110
Acceso en línea:	https://doaj.org/article/c711af355c074708851ede2fe36634a3
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id	oai:doaj.org-article:c711af355c074708851ede2fe36634a3
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spelling	oai:doaj.org-article:c711af355c074708851ede2fe36634a32021-12-01T13:41:28ZEstimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data1664-462X10.3389/fpls.2021.740322https://doaj.org/article/c711af355c074708851ede2fe36634a32021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fpls.2021.740322/fullhttps://doaj.org/toc/1664-462XLeaf area index (LAI) is an important variable for characterizing plant canopy in crop models. It is traditionally defined as the total one-sided leaf area per unit ground area and is estimated by both direct and indirect methods. This paper explores the effectiveness of using light detection and ranging (LiDAR) data to estimate LAI for sorghum and maize with different treatments at multiple times during the growing season from both a wheeled vehicle and Unmanned Aerial Vehicles. Linear and nonlinear regression models are investigated for prediction utilizing statistical and plant structure-based features extracted from the LiDAR point cloud data with ground reference obtained from an in-field plant canopy analyzer (indirect method). Results based on the value of the coefficient of determination (R2) and root mean squared error for predictive models ranged from ∼0.4 in the early season to ∼0.6 for sorghum and ∼0.5 to 0.80 for maize from 40 Days after Sowing to harvest.Behrokh NazeriMelba M. CrawfordMelba M. CrawfordMitchell R. TuinstraFrontiers Media S.A.articlehigh-throughput phenotypingremote sensingLiDARleaf area indexmachine learningrow cropsPlant cultureSB1-1110ENFrontiers in Plant Science, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	high-throughput phenotyping remote sensing LiDAR leaf area index machine learning row crops Plant culture SB1-1110
spellingShingle	high-throughput phenotyping remote sensing LiDAR leaf area index machine learning row crops Plant culture SB1-1110 Behrokh Nazeri Melba M. Crawford Melba M. Crawford Mitchell R. Tuinstra Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data
description	Leaf area index (LAI) is an important variable for characterizing plant canopy in crop models. It is traditionally defined as the total one-sided leaf area per unit ground area and is estimated by both direct and indirect methods. This paper explores the effectiveness of using light detection and ranging (LiDAR) data to estimate LAI for sorghum and maize with different treatments at multiple times during the growing season from both a wheeled vehicle and Unmanned Aerial Vehicles. Linear and nonlinear regression models are investigated for prediction utilizing statistical and plant structure-based features extracted from the LiDAR point cloud data with ground reference obtained from an in-field plant canopy analyzer (indirect method). Results based on the value of the coefficient of determination (R2) and root mean squared error for predictive models ranged from ∼0.4 in the early season to ∼0.6 for sorghum and ∼0.5 to 0.80 for maize from 40 Days after Sowing to harvest.
format	article
author	Behrokh Nazeri Melba M. Crawford Melba M. Crawford Mitchell R. Tuinstra
author_facet	Behrokh Nazeri Melba M. Crawford Melba M. Crawford Mitchell R. Tuinstra
author_sort	Behrokh Nazeri
title	Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data
title_short	Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data
title_full	Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data
title_fullStr	Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data
title_full_unstemmed	Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data
title_sort	estimating leaf area index in row crops using wheel-based and airborne discrete return light detection and ranging data
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/c711af355c074708851ede2fe36634a3
work_keys_str_mv	AT behrokhnazeri estimatingleafareaindexinrowcropsusingwheelbasedandairbornediscretereturnlightdetectionandrangingdata AT melbamcrawford estimatingleafareaindexinrowcropsusingwheelbasedandairbornediscretereturnlightdetectionandrangingdata AT melbamcrawford estimatingleafareaindexinrowcropsusingwheelbasedandairbornediscretereturnlightdetectionandrangingdata AT mitchellrtuinstra estimatingleafareaindexinrowcropsusingwheelbasedandairbornediscretereturnlightdetectionandrangingdata
_version_	1718405138464374784

Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data

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