Smart non-contact phenotyping of raspberries and quinces using machine learning methods, hyperspectral and 3D images

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.