The goal of the project is to develop a methodology and tools for non-invasive phenotyping (description and evaluation) of raspberry and Japanese quince yield components based on 3D and hyperspectral imaging and machine learning (ML). To distinguish candidates for cultivars in fruit breeding it is necessary to describe and evaluate the characteristics of several thousand seedlings. This project aims to solve these problems.
On 6th october, researcher Dr.Sc.Comp.Kaspars Sudars and programming engineer Astile
Peter attended Microwave Theory and Techniques in Wireless Communications (MTTW'22)
and successfully presented the paper on "YOLOv5 Deep Neural Network for Quince and
Raspberry Detection on RGB images". In the conference Astile Peter was awarded as the
best presenter in the wireless communication section.