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pyprecag - a Python package for the analysis of precision agriculture data
pyprecag is a Python package containing processing functions used for analysis of precision agricultural data collected by hand or using on-the-go sensors like yield monitors.
pyprecag functions underpin the tools found in PAT - Precision Agriculture Tools plugin for QGIS.
Agricultural Spatial Analysis and Modelling
This project was initiated by CSIRO with support from Wine Australia and the Australia Federal Government’s Department of Agriculture as part of its Rural R&D for Profit program. Development of this QGIS 3 compatible version was supported by CSIRO.
CSIRO Open Source Software Licence (Based on MIT/BSD Open Source Licence)
Ratcliff, Christina; Gobbett, David; Bramley, Rob (2020): pyprecag - a Python package for the analysis of precision agriculture data. v4. CSIRO. Software Collection.
All Rights (including copyright) CSIRO 2020.
The metadata and files (if any) are available to the public.
Python 2.7 & 3.7
CSA1603 Simple tools for spatial analysis - key enabling technologies for Precision and Digital Viticulture (GIS Freeware RRDFP)
Adoption of Precision Viticulture (PV) approaches to grapegrowing and winemaking are greatly constrained by perceptions of
high cost and a large time requirement for data analysis and management; lack of technical support is also a significant problem.
It is highly likely that similar perceptions will constrain adoption of other technologies which ... moremight be inferred by the broader
concept of Digital Viticulture (DV; e.g. fruit and canopy sensing, yield estimation, precision irrigation, Big Data).
Recently, several freeware GIS software programs have become available with advanced functionality which would support most
of the spatial analysis tasks employed in PV. This project will select one of these freeware platforms and use it to implement
automated tools for the spatial analyses which underpin PV/DV thereby making them accessible to industry practitioners. It will
therefore be a key enabler for the development and advancement of DV in particular with respect to issues such as (i)
simultaneous yield, crop condition and quality estimation and forecasting, and (ii) dynamic canopy, disease and water
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