  Training real-world neural network models to achieve high performance and
generalizability typically requires a substantial amount of labeled data,
spanning a broad range of variation. This data-labeling process can be both
labor and cost intensive. To achieve desirable predictive performance, a
trained model is typically applied into a domain where the data distribution is
similar to the training dataset. However, for many agricultural machine
learning problems, training datasets are collected at a specific location,
during a specific period in time of the growing season. Since agricultural
systems exhibit substantial variability in terms of crop type, cultivar,
management, seasonal growth dynamics, lighting condition, sensor type, etc, a
model trained from one dataset often does not generalize well across domains.
To enable more data efficient and generalizable neural network models in
agriculture, we propose a method that generates photorealistic agricultural
images from a synthetic 3D crop model domain into real world crop domains. The
method uses a semantically constrained GAN (generative adversarial network) to
preserve the fruit position and geometry. We observe that a baseline CycleGAN
method generates visually realistic target domain images but does not preserve
fruit position information while our method maintains fruit positions well.
Image generation results in vineyard grape day and night images show the visual
outputs of our network are much better compared to a baseline network.
Incremental training experiments in vineyard grape detection tasks show that
the images generated from our method can significantly speed the domain
adaption process, increase performance for a given number of labeled images
(i.e. data efficiency), and decrease labeling requirements.
