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- W4382867111 abstract "Deep learning methods can provide accurate segmentations for plant monitoring in precision agriculture. However, acquiring and annotating a sufficient number of images to train deep segmentation models is a tedious and time-consuming task. This drawback can be tackled by means of data augmentation methods, and recently mixing methods have achieved good results. In this work, 11 mixing data augmentation methods have been applied to construct segmentation models of natural images of a vineyard. The experiments show that by applying these mixing strategies, the performance of the segmentation models can be 4.90% better than models trained with traditional data augmentation techniques." @default.
- W4382867111 created "2023-07-02" @default.
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- W4382867111 date "2023-07-02" @default.
- W4382867111 modified "2023-09-23" @default.
- W4382867111 title "50. Data augmentation techniques for grape bunch segmentation in natural images" @default.
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- W4382867111 doi "https://doi.org/10.3920/978-90-8686-947-3_50" @default.
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