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Machine learning to extract maximum value from soil and crop variability, Paddocks pre-processed ML input datasets

Adelaide University
David, Rakesh ; Schilling, Rhiannon ; McDonald, Glenn
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25909/19158419.v2&rft.title=Machine learning to extract maximum value from soil and crop variability, Paddocks pre-processed ML input datasets&rft.identifier=10.25909/19158419.v2&rft.publisher=The University of Adelaide&rft.description=Pre-processed ML input data for 4 Roseworthy paddocks, B4, B3, E2, E5. Files suffixed with paddock names, includes read me file for all paddock dataThe collection includes 4 paddocks with data including paddock boundaries, crop yield, EM38 geophysics, elevation, yield associated moisture percentage. The data accessible from the paddocks and has been acquired between 2005 and 2020. Pre-processed data for machine learning analytics. Pre-processed data was converted to standard csv machine-readable format with CRS included for all measurements. Includes processed paddock measurements, pre-processed Remote Sensing time-series data (Landsat, resampled to 5-m resolution using bilinear interpolation) and pre-processed climate time-series data (SILO database). Readme metadata documents of processed files to assist for ML purposes. Measurements re-scaled and spatially aligned using ordinary block kriging method using locally estimated variograms. The value at each grid point represents an average interpolated value within a 5-m block, centred at the grid point. &rft.creator=David, Rakesh &rft.creator=Schilling, Rhiannon &rft.creator=McDonald, Glenn &rft.edition=2&rft_rights= https://creativecommons.org/licenses/by-nc/4.0/&rft_subject=Agricultural land management&rft_subject=Agricultural management of nutrients&rft_subject=Agronomy&rft_subject=Roseworthy paddocks&rft_subject=Machine Learning&rft_subject=EM38&rft_subject=crop yield&rft.type=dataset&rft.language=English Access the data
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Pre-processed ML input data for 4 Roseworthy paddocks, B4, B3, E2, E5. Files suffixed with paddock names, includes read me file for all paddock data

The collection includes 4 paddocks with data including paddock boundaries, crop yield, EM38 geophysics, elevation, yield associated moisture percentage. The data accessible from the paddocks and has been acquired between 2005 and 2020. Pre-processed data for machine learning analytics. Pre-processed data was converted to standard csv machine-readable format with CRS included for all measurements. Includes processed paddock measurements, pre-processed Remote Sensing time-series data (Landsat, resampled to 5-m resolution using bilinear interpolation) and pre-processed climate time-series data (SILO database). Readme metadata documents of processed files to assist for ML purposes. Measurements re-scaled and spatially aligned using ordinary block kriging method using locally estimated variograms. The value at each grid point represents an average interpolated value within a 5-m block, centred at the grid point.

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Subjects
Agricultural land management | Agricultural management of nutrients | Agronomy | EM38 | Machine Learning | Roseworthy paddocks | crop yield |

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Identifiers
ACN 633 798 857