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Found 6 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.
Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks
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.
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 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.
