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Improving Deep Learning Predictions with Simulated Images, and Vice Versa
In this paper, we systematically vary amounts of real and synthetic images used for training in both maize and canola to better understand situations where synthetic images generated from L-systems can help prediction on real images.
LAESI: Leaf Area Estimation with Synthetic Imagery
We introduce LAESI, a Synthetic Leaf Dataset of 100, 000 synthetic leaf images on millimeter paper, each with semantic masks and surface area labels.
Generating Diverse Agricultural Data for Vision-Based Farming Applications
The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data.
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