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Reason: UA Roseworthy data collection. Please contact Rhiannon Schilling (PIRSA-SARDI) Rhiannon.Schilling@sa.gov.au to request access.

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

Version 2 2024-02-20, 05:08
Version 1 2022-02-28, 00:49
dataset
posted on 2022-02-28, 00:49 authored by Rakesh DavidRakesh David, Rhiannon SchillingRhiannon Schilling, Glenn McDonaldGlenn McDonald
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.

Funding

GRDC: UOA2002-007RTX

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