Here, we introduce a novel RGB-D image database (WE3DS) for semantic segmentation in crop farming. It contains 2,568 RGB-D images (color image and distance map) and hand-annotated ground-truth masks for semantic segmentation and is the first RGB-D image dataset for multi-class plant species semantic segmentation task. Images were taken under natural light conditions using an RGB-D sensor consisting of two RGB cameras in a stereo setup. Please cite the original source when using this dataset. Kitzler, F.; Barta, N.; Neugschwandtner, R.W.; Gronauer, A.; Motsch, V. WE3DS: An RGB-D Image Dataset for Semantic Segmentation in Agriculture. Sensors 2023, 23, 2713. https://doi.org/10.3390/s23052713

{"references": ["Kitzler, F.; Barta, N.; Neugschwandtner, R.W.; Gronauer, A.; Motsch, V. WE3DS: An RGB-D Image Dataset for Semantic Segmentation in Agriculture. Sensors 2023, 23, 2713. https://doi.org/10.3390/s23052713"]}

The project "DiLaAg – Digitalization and Innovation Laboratory in Agricultural Sciences" was supported by the Government of Lower Austria and the private foundation Forum Morgen.