Towards Automated Yield Estimation in Viticulture

Forecasting the grape yield of vineyards is of critical importance in the wine industry, as it allows grape growers to more accurately and confidently invests in capital equipment, negotiate pricing, schedule labour and develop marketing strategies. Currently, industry standard forecasts are generated by manual sampling of bunch weights, grape size, grape numbers and seasonal predictions which takes significant time and effort and thus can sample a very small proportion of the vines. The first step in automating this procedure is to accurately estimate the weight of fruit on the vine and thus this paper presents a survey of the automated image processing methods which have been applied to this problem. Using manually harvested bunches photographed in a laboratory environment, the contribution of various bunch parameters to the weight of a bunch was examined to provide a baseline for the accuracy of weight calculation methods. Then, several recent colour classification methods were compared using images of grapes in vivo. The results showed that a linear regression approach to bunch weight estimation using berry number, bunch perimeter, bunch area and estimated bunch volume was accurate to within 5.3%. Results from in vivo colour classification showed a weight prediction accuracy of 4.5% on a much smaller dataset, demonstrating the promise of this approach in achieving grape grower yield estimation targets.