Aerial Imagery Analysis – Quantifying Appearance and Number of Sorghum Heads for Applications in Breeding and Agronomy
Sorghum (Sorghum bicolor L. Moench) is a C4 tropical grass that plays an essential role in providing nutrition to humans and livestock, particularly in marginal rainfall environments. The timing of head development and the number of heads per unit area are key adaptation traits to consider in agrono...
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doaj-0fe502614d764b6fb83a520a64fb66a52020-11-25T00:17:35ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2018-10-01910.3389/fpls.2018.01544409771Aerial Imagery Analysis – Quantifying Appearance and Number of Sorghum Heads for Applications in Breeding and AgronomyWei Guo0Bangyou Zheng1Andries B. Potgieter2Julien Diot3Kakeru Watanabe4Koji Noshita5David R. Jordan6Xuemin Wang7James Watson8Seishi Ninomiya9Scott C. Chapman10Scott C. Chapman11International Field Phenomics Research Laboratory, Institute for Sustainable Agro-ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, JapanAgriculture and Food – Commonwealth Scientific and Industrial Research Organisation, St Lucia, QLD, AustraliaQueensland Alliance for Agriculture and Food Innovation, The University of Queensland, Toowoomba, QLD, AustraliaMontpellier SupAgro, Montpellier, FranceLaboratory of Biometry and Bioinformatics, Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, JapanLaboratory of Biometry and Bioinformatics, Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, JapanQueensland Alliance for Agriculture and Food Innovation, The University of Queensland, Warwick, QLD, AustraliaQueensland Alliance for Agriculture and Food Innovation, The University of Queensland, Warwick, QLD, AustraliaQueensland Alliance for Agriculture and Food Innovation, The University of Queensland, Toowoomba, QLD, AustraliaInternational Field Phenomics Research Laboratory, Institute for Sustainable Agro-ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, JapanAgriculture and Food – Commonwealth Scientific and Industrial Research Organisation, St Lucia, QLD, AustraliaSchool of Agriculture and Food Sciences, The University of Queensland, Gatton, QLD, AustraliaSorghum (Sorghum bicolor L. Moench) is a C4 tropical grass that plays an essential role in providing nutrition to humans and livestock, particularly in marginal rainfall environments. The timing of head development and the number of heads per unit area are key adaptation traits to consider in agronomy and breeding but are time consuming and labor intensive to measure. We propose a two-step machine-based image processing method to detect and count the number of heads from high-resolution images captured by unmanned aerial vehicles (UAVs) in a breeding trial. To demonstrate the performance of the proposed method, 52 images were manually labeled; the precision and recall of head detection were 0.87 and 0.98, respectively, and the coefficient of determination (R2) between the manual and new methods of counting was 0.84. To verify the utility of the method in breeding programs, a geolocation-based plot segmentation method was applied to pre-processed ortho-mosaic images to extract >1000 plots from original RGB images. Forty of these plots were randomly selected and labeled manually; the precision and recall of detection were 0.82 and 0.98, respectively, and the coefficient of determination between manual and algorithm counting was 0.56, with the major source of error being related to the morphology of plants resulting in heads being displayed both within and outside the plot in which the plants were sown, i.e., being allocated to a neighboring plot. Finally, the potential applications in yield estimation from UAV-based imagery from agronomy experiments and scouting of production fields are also discussed.https://www.frontiersin.org/article/10.3389/fpls.2018.01544/fullhigh-throughput phenotypingUAV remote sensingsorghum head detecting and countingbreeding fieldimage analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wei Guo Bangyou Zheng Andries B. Potgieter Julien Diot Kakeru Watanabe Koji Noshita David R. Jordan Xuemin Wang James Watson Seishi Ninomiya Scott C. Chapman Scott C. Chapman |
spellingShingle |
Wei Guo Bangyou Zheng Andries B. Potgieter Julien Diot Kakeru Watanabe Koji Noshita David R. Jordan Xuemin Wang James Watson Seishi Ninomiya Scott C. Chapman Scott C. Chapman Aerial Imagery Analysis – Quantifying Appearance and Number of Sorghum Heads for Applications in Breeding and Agronomy Frontiers in Plant Science high-throughput phenotyping UAV remote sensing sorghum head detecting and counting breeding field image analysis |
author_facet |
Wei Guo Bangyou Zheng Andries B. Potgieter Julien Diot Kakeru Watanabe Koji Noshita David R. Jordan Xuemin Wang James Watson Seishi Ninomiya Scott C. Chapman Scott C. Chapman |
author_sort |
Wei Guo |
title |
Aerial Imagery Analysis – Quantifying Appearance and Number of Sorghum Heads for Applications in Breeding and Agronomy |
title_short |
Aerial Imagery Analysis – Quantifying Appearance and Number of Sorghum Heads for Applications in Breeding and Agronomy |
title_full |
Aerial Imagery Analysis – Quantifying Appearance and Number of Sorghum Heads for Applications in Breeding and Agronomy |
title_fullStr |
Aerial Imagery Analysis – Quantifying Appearance and Number of Sorghum Heads for Applications in Breeding and Agronomy |
title_full_unstemmed |
Aerial Imagery Analysis – Quantifying Appearance and Number of Sorghum Heads for Applications in Breeding and Agronomy |
title_sort |
aerial imagery analysis – quantifying appearance and number of sorghum heads for applications in breeding and agronomy |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Plant Science |
issn |
1664-462X |
publishDate |
2018-10-01 |
description |
Sorghum (Sorghum bicolor L. Moench) is a C4 tropical grass that plays an essential role in providing nutrition to humans and livestock, particularly in marginal rainfall environments. The timing of head development and the number of heads per unit area are key adaptation traits to consider in agronomy and breeding but are time consuming and labor intensive to measure. We propose a two-step machine-based image processing method to detect and count the number of heads from high-resolution images captured by unmanned aerial vehicles (UAVs) in a breeding trial. To demonstrate the performance of the proposed method, 52 images were manually labeled; the precision and recall of head detection were 0.87 and 0.98, respectively, and the coefficient of determination (R2) between the manual and new methods of counting was 0.84. To verify the utility of the method in breeding programs, a geolocation-based plot segmentation method was applied to pre-processed ortho-mosaic images to extract >1000 plots from original RGB images. Forty of these plots were randomly selected and labeled manually; the precision and recall of detection were 0.82 and 0.98, respectively, and the coefficient of determination between manual and algorithm counting was 0.56, with the major source of error being related to the morphology of plants resulting in heads being displayed both within and outside the plot in which the plants were sown, i.e., being allocated to a neighboring plot. Finally, the potential applications in yield estimation from UAV-based imagery from agronomy experiments and scouting of production fields are also discussed. |
topic |
high-throughput phenotyping UAV remote sensing sorghum head detecting and counting breeding field image analysis |
url |
https://www.frontiersin.org/article/10.3389/fpls.2018.01544/full |
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