Abstract
Estimating aboveground biomass is important for monitoring crop growth in agronomic field experiments. Often this estimation is done manually, destructively (mowing) or not (counting) on a relatively limited number of sub-plots within an experiment. In the presence of spatial heterogeneity in experiment fields, sensors developed for precision agriculture, have shown great potential to automate this estimation efficiently and provide a spatially continuous measurement over an entire plot. This study investigated the suitability of using an unmanned aerial vehicle (UAV) for biomass and yield estimations in an agronomic field experiment. The main objectives of this work were to compare the estimates made from manual field sampling with those made from UAV data and finally to calculate the improvement that can be expected from the use of UAVs. A 6-ha maize field was studied, with plot treatments for the study of the exogenous organic matter (EOM) amendment effect on crop development. 3D surface models were created from high resolution UAV RGB imagery, before crop emergence and during crop development. The difference between both surface models resulted in crop height which was evaluated against 38 reference points with an R2 of 0.9 and prediction error of 0.16 m. Regression models were used to predict above-ground biomass and grain yield (fresh or dry). Dried grain yield prediction with a generalized additive model gave an error of 0.8 t ha−1 calculated on 100 in-field validation measurements, corresponding to a relative error of 14.77%. UAV-based yield estimates from dry biomass were 15% more accurate than manual yield estimation.
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Acknowledgements
The authors are grateful to Jean-Noel Rampon and Vincent Mercier, in charge in 2014 of the daily management of the field experiment, for the maize sampling and analysis.
Funding
This publication benefited from financial support from the scientific direction of AgroParisTech and from the French national observatory networks “SOERE PRO” part of the AnaEE-France project of the French Investments for the Future (Investissements d'Avenir) program, implemented by the French National Research Agency (ANR) (ANR-11-INBS-0001). The field experiment QualiAgro has been managed since 1998 within a cooperation between INRAE and Veolia Research and Innovation.
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SH and JMG designed the experiments. Experiments were performed by JMG, JM and DH. JMG drafted the manuscript with help and contributions from all other authors. Analyses, figures and tables were mainly performed and produced by JMG. All authors read and approved the final manuscript.
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Gilliot, J.M., Michelin, J., Hadjard, D. et al. An accurate method for predicting spatial variability of maize yield from UAV-based plant height estimation: a tool for monitoring agronomic field experiments. Precision Agric 22, 897–921 (2021). https://doi.org/10.1007/s11119-020-09764-w
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DOI: https://doi.org/10.1007/s11119-020-09764-w