Next Article in Journal
Early Detection of Mold-Contaminated Peanuts Using Machine Learning and Deep Features Based on Optical Coherence Tomography
Previous Article in Journal
Design, Development and Testing of Feeding Grippers for Vegetable Plug Transplanters
Previous Article in Special Issue
Dicamba Injury on Soybean Assessed Visually and with Spectral Vegetation Index
Article

Climate-Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite-Based Deep-Learning Method for Estimating Pasture Yield

1
Joint Remote Sensing Research Centre, University of Queensland, St Lucia, Brisbane, QLD 4072, Australia
2
Grazing Land Systems/Remote Sensing Centre, Queensland Department of Environment and Science, Eco-Sciences Precinct, Dutton Park, Brisbane, QLD 4102, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Gabriel A.e.S. Ferraz and Giuseppe Rossi
AgriEngineering 2021, 3(3), 681-702; https://doi.org/10.3390/agriengineering3030044
Received: 8 August 2021 / Revised: 3 September 2021 / Accepted: 6 September 2021 / Published: 10 September 2021
(This article belongs to the Special Issue Novel Approaches for Unmanned Aerial Vehicle)
The aim of this research is to expand recent developments in the mapping of pasture yield with remotely piloted aircraft systems to that of satellite-borne imagery. To date, spatially explicit and accurate information of the pasture resource base is needed for improved climate-adapted livestock rangeland grazing. This study developed deep learning predictive models of pasture yield, as total standing dry matter in tonnes per hectare (TSDM (tha−1)), from field measurements and both remotely piloted aircraft systems and satellite imagery. Repeated remotely piloted aircraft system structure measurements derived from structure from motion photogrammetry provided measures of pasture biomass from many overlapping high-resolution images. These measurements were taken throughout a growing season and were modelled with persistent photosynthetic pasture responses from various Planet Dove high spatial resolution satellite image-derived vegetation indices. Pasture height modelling as an input to the modelling of yield was assessed against terrestrial laser scanning and reported correlation coefficients (R2) from 0.3 to 0.8 for both a coastal grassland and inland woodland pasture. Accuracy of the predictive modelling from both the remotely piloted aircraft system and the Planet Dove satellite image estimates of pasture yield ranged from 0.8 to 1.8 TSDM (tha−1). These results indicated that the practical application of repeated remotely piloted aircraft system derived measures of pasture yield can, with some limitations, be scaled-up to satellite-borne imagery to provide more temporally and spatially explicit measures of the pasture resource base. View Full-Text
Keywords: remotely piloted aircraft system; structure from motion; photogrammetry; artificial neural networks; deep-learning remotely piloted aircraft system; structure from motion; photogrammetry; artificial neural networks; deep-learning
Show Figures

Figure 1

MDPI and ACS Style

Barnetson, J.; Phinn, S.; Scarth, P. Climate-Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite-Based Deep-Learning Method for Estimating Pasture Yield. AgriEngineering 2021, 3, 681-702. https://doi.org/10.3390/agriengineering3030044

AMA Style

Barnetson J, Phinn S, Scarth P. Climate-Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite-Based Deep-Learning Method for Estimating Pasture Yield. AgriEngineering. 2021; 3(3):681-702. https://doi.org/10.3390/agriengineering3030044

Chicago/Turabian Style

Barnetson, Jason, Stuart Phinn, and Peter Scarth. 2021. "Climate-Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite-Based Deep-Learning Method for Estimating Pasture Yield" AgriEngineering 3, no. 3: 681-702. https://doi.org/10.3390/agriengineering3030044

Find Other Styles

Article Access Map by Country/Region

1
Back to TopTop