Segmentation of wine berries

Dataset contains high resolution images collected with a moving field phenotyping platform, the Phenoliner.
The collected images show 3 different varieties (Riesling, Felicia, Regent) in 2 different training systems (VSP=vertical shoot positioning and SMPH= semi minimal pruned hedges), collected in 2 points in time (before and after thinning) in 2018. For each image we provide a manual masks which allow the identification of single berries.
The folder contains: 1. List with image details (imagename, acquisition date, year, variety, training system and variety number)and 2. Dataset folder with 2 subfolders, namely 1. img – 42 original RGB images and 2. lbl – 42 corresponding labels (manual annotation, with berry, edge, background definition)
The data were used to train a neural network with the main goal to detect single berries in images. The method is described in detail in the specified papers.