Literature DB >> 33664755

High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation.

Leonardo Volpato1, Francisco Pinto2, Lorena González-Pérez2, Iyotirindranath Gilberto Thompson2, Aluízio Borém1, Matthew Reynolds2, Bruno Gérard2, Gemma Molero2,3, Francelino Augusto Rodrigues2.   

Abstract

Plant height (PH) is an essential trait in the screening of most crops. While in crops such as wheat, medium stature helps reduce lodging, tall plants are preferred to increase total above-ground biomass. PH is an easy trait to measure manually, although it can be labor-intense depending on the number of plots. There is an increasing demand for alternative approaches to estimate PH in a higher throughput mode. Crop surface models (CSMs) derived from dense point clouds generated via aerial imagery could be used to estimate PH. This study evaluates PH estimation at different phenological stages using plot-level information from aerial imaging-derived 3D CSM in wheat inbred lines during two consecutive years. Multi-temporal and high spatial resolution images were collected by fixed-wing (P l a t F W ) and multi-rotor (P l a t M R ) unmanned aerial vehicle (UAV) platforms over two wheat populations (50 and 150 lines). The PH was measured and compared at four growth stages (GS) using ground-truth measurements (PHground) and UAV-based estimates (PHaerial). The CSMs generated from the aerial imagery were validated using ground control points (GCPs) as fixed reference targets at different heights. The results show that PH estimations using P l a t F W were consistent with those obtained from P l a t M R , showing some slight differences due to image processing settings. The GCPs heights derived from CSM showed a high correlation and low error compared to their actual heights (R 2 ≥ 0.90, RMSE ≤ 4 cm). The coefficient of determination (R 2) between PHground and PHaerial at different GS ranged from 0.35 to 0.88, and the root mean square error (RMSE) from 0.39 to 4.02 cm for both platforms. In general, similar and higher heritability was obtained using PHaerial across different GS and years and ranged according to the variability, and environmental error of the PHground observed (0.06-0.97). Finally, we also observed high Spearman rank correlations (0.47-0.91) and R 2 (0.63-0.95) of PHaerial adjusted and predicted values against PHground values. This study provides an example of the use of UAV-based high-resolution RGB imagery to obtain time-series estimates of PH, scalable to tens-of-thousands of plots, and thus suitable to be applied in plant wheat breeding trials.
Copyright © 2021 Volpato, Pinto, González-Pérez, Thompson, Borém, Reynolds, Gérard, Molero and Rodrigues.

Entities:  

Keywords:  RGB camera; adjusted and predicted genotypic values; dense point cloud; drones; multi-temporal crop surface model; post-processed kinematic; structure from motion; wheat breeding

Year:  2021        PMID: 33664755      PMCID: PMC7921806          DOI: 10.3389/fpls.2021.591587

Source DB:  PubMed          Journal:  Front Plant Sci        ISSN: 1664-462X            Impact factor:   5.753


  42 in total

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2.  Genetic control of plant height in European winter wheat cultivars.

Authors:  Tobias Würschum; Simon M Langer; C Friedrich H Longin
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3.  A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform.

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4.  Spatiotemporal Interpolation for Environmental Modelling.

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5.  High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates.

Authors:  Simon Madec; Fred Baret; Benoît de Solan; Samuel Thomas; Dan Dutartre; Stéphane Jezequel; Matthieu Hemmerlé; Gallian Colombeau; Alexis Comar
Journal:  Front Plant Sci       Date:  2017-11-27       Impact factor: 5.753

6.  High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling.

Authors:  Kakeru Watanabe; Wei Guo; Keigo Arai; Hideki Takanashi; Hiromi Kajiya-Kanegae; Masaaki Kobayashi; Kentaro Yano; Tsuyoshi Tokunaga; Toru Fujiwara; Nobuhiro Tsutsumi; Hiroyoshi Iwata
Journal:  Front Plant Sci       Date:  2017-03-28       Impact factor: 5.753

Review 7.  Crop Phenomics: Current Status and Perspectives.

Authors:  Chunjiang Zhao; Ying Zhang; Jianjun Du; Xinyu Guo; Weiliang Wen; Shenghao Gu; Jinglu Wang; Jiangchuan Fan
Journal:  Front Plant Sci       Date:  2019-06-03       Impact factor: 5.753

8.  UAV-based imaging platform for monitoring maize growth throughout development.

Authors:  Sara B Tirado; Candice N Hirsch; Nathan M Springer
Journal:  Plant Direct       Date:  2020-06-08

9.  Field-based high-throughput phenotyping of plant height in sorghum using different sensing technologies.

Authors:  Xu Wang; Daljit Singh; Sandeep Marla; Geoffrey Morris; Jesse Poland
Journal:  Plant Methods       Date:  2018-07-04       Impact factor: 4.993

10.  An efficient RGB-UAV-based platform for field almond tree phenotyping: 3-D architecture and flowering traits.

Authors:  Francisca López-Granados; Jorge Torres-Sánchez; Francisco M Jiménez-Brenes; Octavio Arquero; María Lovera; Ana I de Castro
Journal:  Plant Methods       Date:  2019-12-26       Impact factor: 4.993

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Review 3.  Functional phenomics for improved climate resilience in Nordic agriculture.

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4.  Identification lodging degree of wheat using point cloud data and convolutional neural network.

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Journal:  Front Plant Sci       Date:  2022-09-27       Impact factor: 6.627

Review 5.  Domestication of newly evolved hexaploid wheat-A journey of wild grass to cultivated wheat.

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Journal:  Front Genet       Date:  2022-10-03       Impact factor: 4.772

Review 6.  Harnessing Crop Wild Diversity for Climate Change Adaptation.

Authors:  Andrés J Cortés; Felipe López-Hernández
Journal:  Genes (Basel)       Date:  2021-05-20       Impact factor: 4.096

  6 in total

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