WINETECH Technical Yearbook 2019

Figuur: Illustrasie van ML

Machine learning algorithm

• No individual variable can be used to model wine grape yield. • Using RS data for wine grape yield modelling is very complex as other (non- remote sensing) factors often have a (more) substantial impact on yield. • The accuracy of the models are strongly driven by cultivar (with Chenin blanc and Colombar being the most successful) and by region (with the Olifants River region being the most successful). • Weekly FL variables generally produced stronger models than (aggregated) monthly and seasonal variables. WHAT FUTURE RESEARCH IS RECOM- MENDED? The research team recommends that the wine industry develops and maintains a standardised geographical database of vineyards and their related attributes, seeing that data inaccuracy or completeness were

barriers to research. Sawis records, as well as the “Fly-over” database from the Western Cape Provincial Department of Agriculture, would serve as a good starting point. More research on wine grape modelling is strongly recommended and to that end other raw satellite data (e.g. from Sentinel-2 and Landsat-8), in addition to the FL data, should be considered. The area considered in the research should also be expanded. Finally, more research into modelling harvest date should be done with specific focus on the use of ML and a factor classification approach. This will define which FL dataset drive the positive results obtained. The current dataset considered served the exploratory work well, although it was too limited to be split into a training and test set for ML while still retaining the seasonal variability aspect, as well as differences between sites and cultivars that also impact on phenology modelling.

Un-labelled databases

Other related data

Labelled samples (training set)

Labelled databases

Human knowledge (or observed data)

– For more information, contact Dr Caren Jarmain at cjarmain@gmail.com.