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 • 21 May 2021 • Jana Kierdorf, Immanuel Weber, Anna Kicherer, Laura Zabawa, Lukas Drees, Ribana Roscher
In this article, we present a method that addresses the challenge of occluded berries with leaves to obtain a more accurate estimate of the number of berries that will enable a better estimate of the harvest.
no code implementations • 29 Apr 2020 • Laura Zabawa, Anna Kicherer, Lasse Klingbeil, Reinhard Töpfer, Heiner Kuhlmann, Ribana Roscher
The experiments presented in this paper show that we are able to detect green berries in images despite of different training systems.
no code implementations • 1 May 2019 • Laura Zabawa, Anna Kicherer, Lasse Klingbeil, Andres Milioto, Reinhard Töpfer, Heiner Kuhlmann, Ribana Roscher
We count single berries in images to avoid the error-prone detection of grapevine clusters.
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 • 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.