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Behind the leaves - Estimation of occluded grapevine berries with conditional generative adversarial networks., , , , , and . CoRR, (2021)An Automated Field Phenotyping Pipeline for Application in Grapevine Research., , , , , , , and . Sensors, 15 (3): 4823-4836 (2015)Grouping Shapley Value Feature Importances of Random Forests for explainable Yield Prediction., , , , , and . CoRR, (2023)Automatic Differentiation of Damaged and Unharmed Grapes Using RGB Images and Convolutional Neural Networks., , , , , , , , and . ECCV Workshops (6), volume 12540 of Lecture Notes in Computer Science, page 347-359. Springer, (2020)Counting of Grapevine Berries in Images via Semantic Segmentation using Convolutional Neural Networks., , , , , and . CoRR, (2020)An Adaptive Approach for Automated Grapevine Phenotyping using VGG-based Convolutional Neural Networks., , , , , and . CoRR, (2018)Detection of Single Grapevine Berries in Images Using Fully Convolutional Neural Networks., , , , , , and . CVPR Workshops, page 2571-2579. Computer Vision Foundation / IEEE, (2019)Detection of Two Different Grapevine Yellows in Vitis vinifera Using Hyperspectral Imaging., , , , , , , , , and 1 other author(s). Remote. Sens., 12 (24): 4151 (2020)SmartSpectrometer - Embedded Optical Spectroscopy for Applications in Agriculture and Industry., , , , , , , , , and . Sensors, 21 (13): 4476 (2021)Determination of Sugars and Acids in Grape Must Using Miniaturized Near-Infrared Spectroscopy., , , , , , , and . Sensors, 23 (11): 5287 (2023)