Search Results for author:
Found 14 papers, 5 papers with code
On Convolutional Vision Transformers for Yield Prediction
While a variety of methods offer good yield prediction on histogrammed remote sensing data, vision Transformers are only sparsely represented in the literature.
Grouping Shapley Value Feature Importances of Random Forests for explainable Yield Prediction
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.
SOCRATES: A Stereo Camera Trap for Monitoring of Biodiversity
This approach employs stereo vision to infer 3D information of natural habitats and is designated as StereO CameRA Trap for monitoring of biodivErSity (SOCRATES).
Extreme Gradient Boosting for Yield Estimation compared with Deep Learning Approaches
While the accuracies reached with those approaches are promising, the needed amounts of data and the ``black-box'' nature can restrict the application of Deep Learning methods.
Overcoming the Distance Estimation Bottleneck in Estimating Animal Abundance with Camera Traps
Until now camera-to-animal distance measurements are derived by laborious, manual and subjective estimation methods.
Automated Distance Estimation for Wildlife Camera Trapping
We propose a fully automatic approach we call AUtomated DIstance esTimation (AUDIT) to estimate camera-to-animal distances.
Overcoming the Distance Estimation Bottleneck in Camera Trap Distance Sampling
Biodiversity crisis is still accelerating.
Automated Identification of Vulnerable Devices in Networks using Traffic Data and Deep Learning
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.
Exploiting Depth Information for Wildlife Monitoring
In this study, we propose an automated camera trap-based approach to detect and identify animals using depth estimation.
Image-based Automated Species Identification: Can Virtual Data Augmentation Overcome Problems of Insufficient Sampling?
The first level of visual data augmentation applies classic approaches of data augmentation and generation of faked images using a GAN approach.
An Adaptive Approach for Automated Grapevine Phenotyping using VGG-based Convolutional Neural Networks
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.
Automated Phenotyping of Epicuticular Waxes of Grapevine Berries Using Light Separation and Convolutional Neural Networks
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 $r=0. 76$.
Efficient identification, localization and quantification of grapevine inflorescences in unprepared field images using Fully Convolutional Networks
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.
Multi-View Semantic Labeling of 3D Point Clouds for Automated Plant Phenotyping
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.
