Shape and Style GAN-based Multispectral Data Augmentation for Crop/Weed Segmentation in Precision Farming
This work addresses data scarcity for deep learning in precision farming, though it appears incremental as it builds on existing GAN techniques for a specific domain.
The paper tackles the challenge of costly data collection for crop/weed segmentation in precision farming by proposing a GAN-based data augmentation method that replaces object patches with artificial ones of varying shapes and styles, demonstrating effectiveness in quantitative experiments on public datasets.
The use of deep learning methods for precision farming is gaining increasing interest. However, collecting training data in this application field is particularly challenging and costly due to the need of acquiring information during the different growing stages of the cultivation of interest. In this paper, we present a method for data augmentation that uses two GANs to create artificial images to augment the training data. To obtain a higher image quality, instead of re-creating the entire scene, we take original images and replace only the patches containing objects of interest with artificial ones containing new objects with different shapes and styles. In doing this, we take into account both the foreground (i.e., crop samples) and the background (i.e., the soil) of the patches. Quantitative experiments, conducted on publicly available datasets, demonstrate the effectiveness of the proposed approach. The source code and data discussed in this work are available as open source.