Generating Diverse Agricultural Data for Vision-Based Farming Applications

Mikolaj Cieslak, Umabharathi Govindarajan, Alejandro Garcia, Anuradha Chandrashekar, Torsten Hädrich, Aleksander Mendoza-Drosik, Dominik L. Michels, Sören Pirk, Chia-Chun Fu, Wojciech Palubicki

CVPRW 2024 pp. 5422-5431

doi:10.1109/CVPRW63382.2024.00551 /cvprw/2024/cieslak2024cvprw-generating/

Abstract

We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. The model simulates distinct growth stages of these plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data. We validate our model’s effectiveness by comparing the synthetic data against real agricultural images, demonstrating its potential to significantly augment training data for machine learning models in agriculture. This approach not only provides a cost-effective solution for generating high-quality, diverse data but also addresses specific needs in agricultural vision tasks that are not fully covered by general-purpose models.

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Cite

Text

Cieslak et al. "Generating Diverse Agricultural Data for Vision-Based Farming Applications." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00551

Markdown

[Cieslak et al. "Generating Diverse Agricultural Data for Vision-Based Farming Applications." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/cieslak2024cvprw-generating/) doi:10.1109/CVPRW63382.2024.00551

BibTeX

@inproceedings{cieslak2024cvprw-generating,
  title     = {{Generating Diverse Agricultural Data for Vision-Based Farming Applications}},
  author    = {Cieslak, Mikolaj and Govindarajan, Umabharathi and Garcia, Alejandro and Chandrashekar, Anuradha and Hädrich, Torsten and Mendoza-Drosik, Aleksander and Michels, Dominik L. and Pirk, Sören and Fu, Chia-Chun and Palubicki, Wojciech},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2024},
  pages     = {5422-5431},
  doi       = {10.1109/CVPRW63382.2024.00551},
  url       = {https://mlanthology.org/cvprw/2024/cieslak2024cvprw-generating/}
}