Dr Scarlett Liu

Research Associate
School of Mechanical & Manufacturing Engineering

UNSW Sydney

Scarlett is a Research Associate in the Mechatronics discipline within the School of Mechanical and Manufacturing Engineering at the University of New South Wales (UNSW Sydney). Her research interests focus on Robotic with experience in Computer Vision, Data Mining and Data Visualisation. She conducts theoretical investigation and development in shoots and grape detection at different vine growing stages from videos cross vineyards, automatic detection of berry size and bunch structure analysis by Computer Vision. She also applies Statistics and Data Mining techniques to estimate yield based on detected information from images.

Automated Yield Estimation in Viticulture by Computer Vision

Currently, industry standard yield predictions in viticulture are generated by manual sampling of the weight and number of components such as bunches and berries. The data collection takes significant time and despite requiring trained labour still results in inaccurate yield estimates which in turn lead to substantial inefficiencies in scheduling and logistics through the entire wine industry supply chain.

Therefore, this thesis introduces an automated visual yield estimation system using computer vision and data mining with the aim of improving crop estimation. Firstly, a biological baseline for yield estimation is proposed and implemented together with novel algorithms for shoot and bunch detection. A method for transforming the object counts extracted from videos of individual rows to a prediction of crop yield without requiring GPS is introduced, with the additional benefit of generating a production variation map during the season. In addition, a novel fully automated 3D bunch reconstruction algorithm has been developed which is based on a single input image to assist berry counting in vivo.

The overall automated visual yield estimation system has thus been demonstrated to be feasible and to reduce the error and cost associated with existing manual sampling procedures. Together, the contributions of this thesis bring not only economic benefits for grape growers and winemakers, but also provide valuable inputs for future work on vine-level management that will improve quality, yields and optimise resource usage, leading to better wine.