159 citations · 245 across the 12 of their papers we have counts for
12 papers
Grouping Shapley Value Feature Importances of Random Forests for explainable Yield Prediction
Florian Huber, Hannes Engler, Anna Kicherer +3
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…
SOCRATES: A Stereo Camera Trap for Monitoring of Biodiversity
Timm Haucke, Hjalmar S. Kühl, Volker Steinhage
The development and application of modern technology is an essential basis for the efficient monitoring of species in natural habitats and landscapes to trace the development of ec…
Extreme Gradient Boosting for Yield Estimation compared with Deep Learning Approaches
Florian Huber, Artem Yushchenko, Benedikt Stratmann +1
Accurate prediction of crop yield before harvest is of great importance for crop logistics, market planning, and food distribution around the world. Yield prediction requires monit…
Automated Distance Estimation for Wildlife Camera Trapping
Peter Johanns, Timm Haucke, Volker Steinhage
The ongoing biodiversity crisis calls for accurate estimation of animal density and abundance to identify sources of biodiversity decline and effectiveness of conservation interven…
Overcoming the Distance Estimation Bottleneck in Estimating Animal Abundance with Camera Traps
Timm Haucke, Hjalmar S. Kühl, Jacqueline Hoyer +1
The biodiversity crisis is still accelerating, despite increasing efforts by the international community. Estimating animal abundance is of critical importance to assess, for examp…
Automated Identification of Vulnerable Devices in Networks using Traffic Data and Deep Learning
Jakob Greis, Artem Yushchenko, Daniel Vogel +2
Many IoT devices are vulnerable to attacks due to flawed security designs and lacking mechanisms for firmware updates or patches to eliminate the security vulnerabilities. Device-t…
Exploiting Depth Information for Wildlife Monitoring
Timm Haucke, Volker Steinhage
Camera traps are a proven tool in biology and specifically biodiversity research. However, camera traps including depth estimation are not widely deployed, despite providing valuab…
Image-based Automated Species Identification: Can Virtual Data Augmentation Overcome Problems of Insufficient Sampling?
Morris Klasen, Dirk Ahrens, Jonas Eberle +1
Automated species identification and delimitation is challenging, particularly in rare and thus often scarcely sampled species, which do not allow sufficient discrimination of infr…
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…