Technology

Procedurally generated synthetic data points of images and corresponding labels can play a crucial role in training deep neural networks in smart farming. Synthetic data helps address the challenge of limited availability of costly real-world data. By using synthetic data, it is possible to generate large and diverse datasets that capture all the relevant features and variability of real-world data, which can be used to train and improve the performance of deep learning models. For example, a deep learning model for plant recognition could be trained on synthetic images of different crops and their corresponding labels, which would help to recognize and distinguish between different types of plants in real-world images. Similarly, a deep learning model for weed detection could be trained on synthetic images of crops with weeds and their corresponding labels, which would help it identify and remove weeds in real-world agricultural settings. Moreover, synthetic data can be generated at a much lower cost and without any ethical concerns or privacy issues.

Common approaches for using synthetic training data rely on the manual design of 3D scene assets by artists. This process is time consuming and expensive and does not easily scale to the requirements of the data-demands of deep neural networks. We provide a fully automatized generative approach for synthetic data that relies on novel biological and physical simulations paired with state-of-the-art computer graphics techniques. Our approach enables generating thousands of images along with annotations in just a few hours, while simultaneously improving the photorealistic qualities. This significantly advances training perception tasks for applications in precision farming.