An accurate method for predicting spatial variability of maize yield from UAV-based plant height estimation: a tool for
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An accurate method for predicting spatial variability of maize yield from UAV‑based plant height estimation: a tool for monitoring agronomic field experiments J. M. Gilliot1 · J. Michelin1 · D. Hadjard1 · S. Houot1 Accepted: 12 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
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 R 2 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. Keywords Unmanned aerial vehicle (UAV) · Crop surface model (CSM) · Yield prediction · Photogrammetry · Biomass estimation
* J. M. Gilliot jean‑[email protected] 1
UMR ECOSYS, INRAE, AgroParisTech, Université Paris-Saclay, 78850 Thiverval‑Grignon, France
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Precision Agriculture
Introduction Precision agriculture (PA) can help to meet the challenge of feeding an increasing population by optimizing the use of crop inputs towards a low-input sustainable agriculture (Zhang et al. 2002; Lindblom et al. 2017). Quantitative estimation of crop vegetative development and health is a major step in determining both the types of interventions and doses of products during crop operations such as fertilization or phytosanitary treatments and for yield prediction. Improvements in crop management related to PA are based on measuring and managing within-field variability of crop development. These measurements have been made possible using global navigation satellite systems (GNSS) and various sensors to exhaustively quantify the current biomass
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