Reducing the error in Yield Estimation: Exploring and Evaluating Novel Yield Factors for impact as part of an already diverse set

Start year: 2024

Summary: Modelling and visualisation of Flowering, Fruit set and Bunch weights in grapevines to improve accuracy of yield estimation. Development of a Machine Learning (ML) tool and Satellite data measurement methods to assist in bunch weight estimation using non-destructive and destructive in field mapping methods, satellite data to model success of flowering process. Research and development of a yield simulation and reporting tool to measure the efficacy of the identified factors to reduce yield prediction error compared to the actual.

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.