![]() ![]() However, it is unclear which data-augmentation methods are effective for 3D plant-part segmentation. Data augmentation has been shown to improve training on small training sets. Especially for 3D semantic segmentation, the collection of training data is highly labour intensitive and time consuming. However, these methods require a large annotated training set to perform well. Since traditional hand-designed methods for point-cloud processing face challenges in generalisation, current methods are based on deep neural network that learn to perform the 3D segmentation based on training data. Introduction: 3D semantic segmentation of plant point clouds is an important step towards automatic plant phenotyping and crop modeling. 2Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen, Netherlands.1Department of Plant Science, Wageningen University and Research, Wageningen, Netherlands. ![]() Bolai Xin 1* Ji Sun 1 Harm Bartholomeus 2 Gert Kootstra 1 ![]()
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