Project description

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.

Related Scientists

    Mg.sc.ing. Ivars Namatēvs

    Researcher

    +371 67558-129
    [protected]
    Ansis Skadiņš
    Ansis Skadiņš

    Technician

    +37167558154
    [protected]
    Dr. sc. comp. Kaspars Sudars

    Researcher

    [protected]
    Mg. sc. comp. Jānis Judvaitis

    Researcher

    +371 67558-182
    [protected]
    Mg. sc. comp. Rihards Balašs

    Researcher

    +371 67558154
    [protected]