Machine learning tasks often require a significant amount of training data
for the resultant network to perform suitably for a given problem in any
domain. In agriculture, dataset sizes are further limited by phenotypical
differences between two plants of the same genotype, often as a result of
differing growing conditions. Synthetically-augmented datasets have shown
promise in improving existing models when real data is not available. In this
paper, we employ a contrastive unpaired translation (CUT) generative
adversarial network (GAN) and simple image processing techniques to translate
indoor plant images to appear as field images. While we train our network to
translate an image containing only a single plant, we show that our method is
easily extendable to produce multiple-plant field images. Furthermore, we use
our synthetic multi-plant images to train several YoloV5 nano object detection
models to perform the task of plant detection and measure the accuracy of the
model on real field data images. Including training data generated by the
CUT-GAN leads to better plant detection performance compared to a network
trained solely on real data.Comment: 35 pages, 23 figure
