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Computer Vision problems deal with the semantic extraction of informationfrom camera images. Especially for field crop images, the underlying problemsare hard to label and even harder to learn, and the availability ofhigh-quality training data is low. Deep neural networks do a good job ofextracting the necessary models from training examples. However, they rely onan abundance of training data that is not feasible to generate or label byexpert annotation. To address this challenge, we make use of the Unreal Engineto render large and complex virtual scenes. We rely on the performance ofindividual nodes by distributing plant simulations across nodes and bothgenerate scenes as well as train neural networks on GPUs, restricting nodecommunication to parallel learning.

Towards Large-Scale Rendering of Simulated Crops for Synthetic Ground Truth Generation on Modular Supercomputers