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High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing

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High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing. / Holman, Fenner Howard; Riche, Andrew; Michalski, Adam; Castle, March; Wooster, Martin John; Hawkesford, Malcolm.

In: REMOTE SENSING, Vol. 8, No. 12, 18.12.2016.

Research output: Contribution to journalArticle

Harvard

Holman, FH, Riche, A, Michalski, A, Castle, M, Wooster, MJ & Hawkesford, M 2016, 'High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing', REMOTE SENSING, vol. 8, no. 12. https://doi.org/10.3390/rs8121031

APA

Holman, F. H., Riche, A., Michalski, A., Castle, M., Wooster, M. J., & Hawkesford, M. (2016). High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing. REMOTE SENSING, 8(12). https://doi.org/10.3390/rs8121031

Vancouver

Holman FH, Riche A, Michalski A, Castle M, Wooster MJ, Hawkesford M. High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing. REMOTE SENSING. 2016 Dec 18;8(12). https://doi.org/10.3390/rs8121031

Author

Holman, Fenner Howard ; Riche, Andrew ; Michalski, Adam ; Castle, March ; Wooster, Martin John ; Hawkesford, Malcolm. / High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing. In: REMOTE SENSING. 2016 ; Vol. 8, No. 12.

Bibtex Download

@article{3e3e9e42b34740c5bcbad8fcee313e14,
title = "High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing",
abstract = "There is a growing need to increase global crop yields, whilst minimising use of resources such as land, fertilisers and water. Agricultural researchers use ground-based observations to identify, select and develop crops with favourable genotypes and phenotypes; however, the ability to collect rapid, high quality and high volume phenotypic data in open fields is restricting this. This study develops and assesses a method for deriving crop height and growth rate rapidly from multi-temporal, very high spatial resolution (1 cm/pixel), 3D digital surface models of crop field trials produced via Structure from Motion (SfM) photogrammetry using aerial imagery collected through repeated campaigns flying an Unmanned Aerial Vehicle (UAV) with a mounted Red Green Blue (RGB) camera. We compare UAV SfM modelled crop heights to those derived from terrestrial laser scanner (TLS) and to the standard field measurement of crop height conducted using a 2 m rule. The most accurate UAV-derived surface model and the TLS both achieve a Root Mean Squared Error (RMSE) of 0.03 m compared to the existing manual 2 m rule method. The optimised UAV method was then applied to the growing season of a winter wheat field phenotyping experiment containing 25 different varieties grown in 27 m2 plots and subject to four different nitrogen fertiliser treatments. Accuracy assessments at different stages of crop growth produced consistently low RMSE values (0.07, 0.02 and 0.03 m for May, June and July, respectively), enabling crop growth rate to be derived from differencing of the multi-temporal surface models. We find growth rates range from −13 mm/day to 17 mm/day. Our results clearly display the impact of variable nitrogen fertiliser rates on crop growth. Digital surface models produced provide a novel spatial mapping of crop height variation both at the field scale and also within individual plots. This study proves UAV based SfM has the potential to become a new standard for high-throughput phenotyping of in-field crop heights.",
keywords = "unmanned aerial vehicle (UAV); structure from motion (SfM); light detection and ranging (LiDAR); peat fire; depth of burn (DoB); carbon emissions; Indonesia; reducing emissions from deforestation and forest degradation (REDD+), Structure from Motion, photogrammetry, crop height, phenotyping",
author = "Holman, {Fenner Howard} and Andrew Riche and Adam Michalski and March Castle and Wooster, {Martin John} and Malcolm Hawkesford",
year = "2016",
month = "12",
day = "18",
doi = "10.3390/rs8121031",
language = "English",
volume = "8",
journal = "REMOTE SENSING",
issn = "2072-4292",
publisher = "Multidisciplinary Digital Publishing Institute",
number = "12",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing

AU - Holman, Fenner Howard

AU - Riche, Andrew

AU - Michalski, Adam

AU - Castle, March

AU - Wooster, Martin John

AU - Hawkesford, Malcolm

PY - 2016/12/18

Y1 - 2016/12/18

N2 - There is a growing need to increase global crop yields, whilst minimising use of resources such as land, fertilisers and water. Agricultural researchers use ground-based observations to identify, select and develop crops with favourable genotypes and phenotypes; however, the ability to collect rapid, high quality and high volume phenotypic data in open fields is restricting this. This study develops and assesses a method for deriving crop height and growth rate rapidly from multi-temporal, very high spatial resolution (1 cm/pixel), 3D digital surface models of crop field trials produced via Structure from Motion (SfM) photogrammetry using aerial imagery collected through repeated campaigns flying an Unmanned Aerial Vehicle (UAV) with a mounted Red Green Blue (RGB) camera. We compare UAV SfM modelled crop heights to those derived from terrestrial laser scanner (TLS) and to the standard field measurement of crop height conducted using a 2 m rule. The most accurate UAV-derived surface model and the TLS both achieve a Root Mean Squared Error (RMSE) of 0.03 m compared to the existing manual 2 m rule method. The optimised UAV method was then applied to the growing season of a winter wheat field phenotyping experiment containing 25 different varieties grown in 27 m2 plots and subject to four different nitrogen fertiliser treatments. Accuracy assessments at different stages of crop growth produced consistently low RMSE values (0.07, 0.02 and 0.03 m for May, June and July, respectively), enabling crop growth rate to be derived from differencing of the multi-temporal surface models. We find growth rates range from −13 mm/day to 17 mm/day. Our results clearly display the impact of variable nitrogen fertiliser rates on crop growth. Digital surface models produced provide a novel spatial mapping of crop height variation both at the field scale and also within individual plots. This study proves UAV based SfM has the potential to become a new standard for high-throughput phenotyping of in-field crop heights.

AB - There is a growing need to increase global crop yields, whilst minimising use of resources such as land, fertilisers and water. Agricultural researchers use ground-based observations to identify, select and develop crops with favourable genotypes and phenotypes; however, the ability to collect rapid, high quality and high volume phenotypic data in open fields is restricting this. This study develops and assesses a method for deriving crop height and growth rate rapidly from multi-temporal, very high spatial resolution (1 cm/pixel), 3D digital surface models of crop field trials produced via Structure from Motion (SfM) photogrammetry using aerial imagery collected through repeated campaigns flying an Unmanned Aerial Vehicle (UAV) with a mounted Red Green Blue (RGB) camera. We compare UAV SfM modelled crop heights to those derived from terrestrial laser scanner (TLS) and to the standard field measurement of crop height conducted using a 2 m rule. The most accurate UAV-derived surface model and the TLS both achieve a Root Mean Squared Error (RMSE) of 0.03 m compared to the existing manual 2 m rule method. The optimised UAV method was then applied to the growing season of a winter wheat field phenotyping experiment containing 25 different varieties grown in 27 m2 plots and subject to four different nitrogen fertiliser treatments. Accuracy assessments at different stages of crop growth produced consistently low RMSE values (0.07, 0.02 and 0.03 m for May, June and July, respectively), enabling crop growth rate to be derived from differencing of the multi-temporal surface models. We find growth rates range from −13 mm/day to 17 mm/day. Our results clearly display the impact of variable nitrogen fertiliser rates on crop growth. Digital surface models produced provide a novel spatial mapping of crop height variation both at the field scale and also within individual plots. This study proves UAV based SfM has the potential to become a new standard for high-throughput phenotyping of in-field crop heights.

KW - unmanned aerial vehicle (UAV); structure from motion (SfM); light detection and ranging (LiDAR); peat fire; depth of burn (DoB); carbon emissions; Indonesia; reducing emissions from deforestation and forest degradation (REDD+)

KW - Structure from Motion

KW - photogrammetry

KW - crop height

KW - phenotyping

U2 - 10.3390/rs8121031

DO - 10.3390/rs8121031

M3 - Article

VL - 8

JO - REMOTE SENSING

JF - REMOTE SENSING

SN - 2072-4292

IS - 12

ER -

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