8 citations · 21 across the 5 of their papers we have counts for
4 papers · 1 filter
An Adaptive Approach for Automated Grapevine Phenotyping using VGG-based Convolutional Neural Networks
Jonatan Grimm, Katja Herzog, Florian Rist +3
In (grapevine) breeding programs and research, periodic phenotyping and multi-year monitoring of different grapevine traits, like growth or yield, is needed especially in the field…
Efficient identification, localization and quantification of grapevine inflorescences in unprepared field images using Fully Convolutional Networks
Robert Rudolph, Katja Herzog, Reinhard Töpfer +1
Yield and its prediction is one of the most important tasks in grapevine breeding purposes and vineyard management. Commonly, this trait is estimated manually right before harvest…
Automated Phenotyping of Epicuticular Waxes of Grapevine Berries Using Light Separation and Convolutional Neural Networks
Pierre Barré, Katja Herzog, Rebecca Höfle +3
In viticulture the epicuticular wax as the outer layer of the berry skin is known as trait which is correlated to resilience towards Botrytis bunch rot. Traditionally this trait is…
Multi-View Semantic Labeling of 3D Point Clouds for Automated Plant Phenotyping
Bernhard Japes, Jennifer Mack, Florian Rist +3
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 managemen…