- We have no prior data related to the variety grown on the block
- We have no prior data related to THIS block, but we do know about similar blocks
- We have a reasonable set of data about the variety and the region.
- A statistical modelling approach
Samples are considered “as a whole”, and each sample is decorated with likely influential factors (e.g. variety and region). Using specialist statistical software, it is then possible to analyse the effect of each factor on ripening speed.
This is an unsupervised machine learning method CSIRO has perfected.
- A physics/chemistry approach
Samples are considered as time series, each vintage being a period of a block ripening signal. Each period is considered independent from the previous periods.
Samples are then analysed to test candidate ripening equations. This is a supervised machine learning method Thoughtpool has developed.
- extend and stabilise predictions in typical weather conditions:
The planning benefits are important as good samples in an easy vintage will result in transient/unreliable dates using OPW (as a result of the linear bias), whereas it will result in stable dates with non linear models.
As a result, less time is spent adapting a plan when there is no need to.
- actually model the effects of atypical weather:
The planning benefits are also important as good samples in a difficult vintage MUST result in fleeting dates to reflect the changing conditions. This additional level of modelling is impossible to conduct over a biased ripening model.
Better maturity prediction
More accurate predictions of grape maturity with a longer planning horizon
Latest updates from the blog …
The prediction strategy we use depends what data we already have about the block or vineyard of interest. There are three possibilities we deal with: We have no prior data related to the variety grown on the block We have no prior data related to THIS block, but we do...read more
The visible part of Harvest-plan is the web application that harvest planners (and their harvest partners) interact with using via desktops, tablets or smartphones. This is used to gather vineyard data and provide access to the maps and analytics based on that data....read more
Harvest-plan aims to ease the vintage planning process by producing non-linear harvest predictions of grape maturity. Non linear predictions can be hard going even for expert mathematicians with specialist statistical modelling software. It calls on a body of...read more