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UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies
Plant growth rate is an essential phenotypic parameter for quantifying potential crop productivity. Under field conditions, manual measurement of plant growth rate is less accurate in most cases. Image-based high-throughput platforms offer great potential for rapid, non-destructive, and objective es...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9611424/ https://www.ncbi.nlm.nih.gov/pubmed/36297713 http://dx.doi.org/10.3390/plants11202691 |
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author | Nielsen, Karsten M. E. Duddu, Hema S. N. Bett, Kirstin E. Shirtliffe, Steve J. |
author_facet | Nielsen, Karsten M. E. Duddu, Hema S. N. Bett, Kirstin E. Shirtliffe, Steve J. |
author_sort | Nielsen, Karsten M. E. |
collection | PubMed |
description | Plant growth rate is an essential phenotypic parameter for quantifying potential crop productivity. Under field conditions, manual measurement of plant growth rate is less accurate in most cases. Image-based high-throughput platforms offer great potential for rapid, non-destructive, and objective estimation of plant growth parameters. The aim of this study was to assess the potential for quantifying plant growth rate using UAV-based (unoccupied aerial vehicle) imagery collected multiple times throughout the growing season. In this study, six diverse lines of lentils were grown in three replicates of 1 m(2) microplots with six biomass collection time-points throughout the growing season over five site-years. Aerial imagery was collected simultaneously with each manual measurement of the above-ground biomass time-point and was used to produce two-dimensional orthomosaics and three-dimensional point clouds. Non-linear logistic models were fit to multiple data collection points throughout the growing season. Overall, remotely detected vegetation area and crop volume were found to produce trends comparable to the accumulation of dry weight biomass throughout the growing season. The growth rate and G50 (days to 50% of maximum growth) parameters of the model effectively quantified lentil growth rate indicating significant potential for image-based tools to be used in plant breeding programs. Comparing image-based groundcover and vegetation volume estimates with manually measured above-ground biomass suggested strong correlations. Vegetation area measured from a UAV has utility in quantifying lentil biomass and is indicative of leaf area early in the growing season. For mid- to late-season biomass estimation, plot volume was determined to be a better estimator. Apart from traditional traits, the estimation and analysis of plant parameters not typically collected in traditional breeding programs are possible with image-based methods, and this can create new opportunities to improve breeding efficiency mainly by offering new phenotypes and affecting selection intensity. |
format | Online Article Text |
id | pubmed-9611424 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96114242022-10-28 UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies Nielsen, Karsten M. E. Duddu, Hema S. N. Bett, Kirstin E. Shirtliffe, Steve J. Plants (Basel) Article Plant growth rate is an essential phenotypic parameter for quantifying potential crop productivity. Under field conditions, manual measurement of plant growth rate is less accurate in most cases. Image-based high-throughput platforms offer great potential for rapid, non-destructive, and objective estimation of plant growth parameters. The aim of this study was to assess the potential for quantifying plant growth rate using UAV-based (unoccupied aerial vehicle) imagery collected multiple times throughout the growing season. In this study, six diverse lines of lentils were grown in three replicates of 1 m(2) microplots with six biomass collection time-points throughout the growing season over five site-years. Aerial imagery was collected simultaneously with each manual measurement of the above-ground biomass time-point and was used to produce two-dimensional orthomosaics and three-dimensional point clouds. Non-linear logistic models were fit to multiple data collection points throughout the growing season. Overall, remotely detected vegetation area and crop volume were found to produce trends comparable to the accumulation of dry weight biomass throughout the growing season. The growth rate and G50 (days to 50% of maximum growth) parameters of the model effectively quantified lentil growth rate indicating significant potential for image-based tools to be used in plant breeding programs. Comparing image-based groundcover and vegetation volume estimates with manually measured above-ground biomass suggested strong correlations. Vegetation area measured from a UAV has utility in quantifying lentil biomass and is indicative of leaf area early in the growing season. For mid- to late-season biomass estimation, plot volume was determined to be a better estimator. Apart from traditional traits, the estimation and analysis of plant parameters not typically collected in traditional breeding programs are possible with image-based methods, and this can create new opportunities to improve breeding efficiency mainly by offering new phenotypes and affecting selection intensity. MDPI 2022-10-12 /pmc/articles/PMC9611424/ /pubmed/36297713 http://dx.doi.org/10.3390/plants11202691 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Nielsen, Karsten M. E. Duddu, Hema S. N. Bett, Kirstin E. Shirtliffe, Steve J. UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies |
title | UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies |
title_full | UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies |
title_fullStr | UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies |
title_full_unstemmed | UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies |
title_short | UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies |
title_sort | uav image-based crop growth analysis of 3d-reconstructed crop canopies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9611424/ https://www.ncbi.nlm.nih.gov/pubmed/36297713 http://dx.doi.org/10.3390/plants11202691 |
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