no code implementations • 14 Apr 2023 • Florian Huber, Hannes Engler, Anna Kicherer, Katja Herzog, Reinhard Töpfer, Volker Steinhage
Explainability in yield prediction helps us fully explore the potential of machine learning models that are already able to achieve high accuracy for a variety of yield prediction scenarios.
no code implementations • 23 Nov 2018 • Jonatan Grimm, Katja Herzog, Florian Rist, Anna Kicherer, Reinhard Töpfer, Volker Steinhage
This work presents a proof-of-concept analyzing RGB images of different growth stages of grapevines with the aim to detect and quantify promising plant organs which are related to yield.
no code implementations • 19 Jul 2018 • Pierre Barré, Katja Herzog, Rebecca Höfle, Matthias B. Hullin, Reinhard Töpfer, Volker Steinhage
In addition, electrical impedance of the cuticle and its epicuticular waxes (described as an indicator for the thickness of berry skin and its permeability) was correlated to the detected proportion of waxes with $r=0. 76$.
no code implementations • 10 Jul 2018 • Robert Rudolph, Katja Herzog, Reinhard Töpfer, Volker Steinhage
Summarized, the presented approach is a promising strategy in order to predict yield potential automatically in the earliest stage of grapevine development which is applicable for objective monitoring and evaluations of breeding material, genetic repositories or commercial vineyards.
no code implementations • 10 May 2018 • Bernhard Japes, Jennifer Mack, Florian Rist, Katja Herzog, Reinhard Töpfer, Volker Steinhage
Semantic labeling of 3D point clouds is important for the derivation of 3D models from real world scenarios in several economic fields such as building industry, facility management, town planning or heritage conservation.
no code implementations • 15 Dec 2017 • Ribana Roscher, Katja Herzog, Annemarie Kunkel, Anna Kicherer, Reinhard Töpfer, Wolfgang Förstner
In the present study an automated image analyzing framework was developed in order to estimate the size of grapevine berries from images in a high-throughput manner.