WINETECH Technical Yearbook 2019

BERNARD MOCKE: Private consultant KEYWORDS: Wine grape yield, machine learning, FruitLook. APRIL 2019 A NEW ERA FOR WINE GRAPE YIELD ESTIMATION

With technology at the fingertips of researchers and producers alike, the possibility exists to not only generate and study vast datasets, but also to find re l at ionships between them through machine learning and statistical analysis. Such relationships could provide valuable information on yield estimation, véraison and harvest date. According to the link https://bit.ly/2K0TV2Q , machine learning (ML) is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. But why is this important? An incredible amount of data has been captured within the wine grape industry in the Western Cape over the years and put to good use, but all of this information could be even more valuable if relationships between datasets could ultimately allow the producer to more accurately estimate yield.

In addition, data capturing can be done not only by traditional means, but also through remote sensing (RS) by satellite through initiatives like FruitLook. This article discusses research funded by Winetech which investigates the use of available “big datasets” for wine grape crop estimation. The research was led by Dr Caren Jarmain and other researchers from Stellenbosch University, with support from WineMS and Vinpro. WHAT WERE THE AIMS OF THIS STUDY? Two specific aspects were investigated: • Whether relationships existed between the spatial datasets available through FruitLook (FL) and vineyard block yield information when statistical analysis and machine learning (ML) approaches were applied. • If these relationships could be used to build a wine grape yield and possibly a harvest date model.

PHOTO 1. Illustration of machine learning.