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Found 8 papers, 0 papers with code
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.
Counting of Grapevine Berries in Images via Semantic Segmentation using Convolutional Neural Networks
The experiments presented in this paper show that we are able to detect green berries in images despite of different training systems.
Detection of Single Grapevine Berries in Images Using Fully Convolutional Neural Networks
We count single berries in images to avoid the error-prone detection of grapevine clusters.
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.
Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields
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.
