Numbering follows the CV. For citation metrics, see Google Scholar.

Selected

Yun, H., Uyehara, I. K., Droutsas, I., Ranario, E., Diepenbrock, C. H., Bailey, B. N., & Earles, J. M. (2026). A vision language model for generating XML-based organ-level plant architecture representations of cowpea from simulated images. Frontiers in Artificial Intelligence, 9, 1844338.

doiarXivcodedataproject

BibTeX 
@article{yun2026visionlanguagemodelgenerating,
  title={A Vision Language Model for Generating XML-Based Organ-Level Plant Architecture Representations of Cowpea From Simulated Images},
  author={Heesup Yun and Isaac Kazuo Uyehara and Ioannis Droutsas and Earl Ranario and Christine H. Diepenbrock and Brian N. Bailey and J. Mason Earles},
  journal={Frontiers in Artificial Intelligence},
  volume={9},
  year={2026},
  publisher={Frontiers Media SA},
  doi={10.3389/frai.2026.1844338},
  url={https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1844338/full}
}

Yun, H., Uyehara, I. K., Ranario, E., Lundqvist, L., Diepenbrock, C. H., Bailey, B. N., & Earles, J. M. (2026). Using Vision Language Foundation Models to Generate Plant Simulation Configurations via In-Context Learning. arXiv preprint arXiv:2603.08930.

arXivcodedataproject

BibTeX 
@misc{yun2026usingvisionlanguagefoundation,
  title={Using Vision Language Foundation Models to Generate Plant Simulation Configurations via In-Context Learning},
  author={Heesup Yun and Isaac Kazuo Uyehara and Earl Ranario and Lars Lundqvist and Christine H. Diepenbrock and Brian N. Bailey and J. Mason Earles},
  year={2026},
  eprint={2603.08930},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2603.08930}
}

Yun, H., Lo, S., Diepenbrock, C. H., Bailey, B. N., & Earles, J. M. (2024). VisTA-SR: Improving the Accuracy and Resolution of Low-Cost Thermal Imaging Cameras for Agriculture. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Agriculture-Vision Workshop (pp. 5470–5479).

pdfcodedataproject

BibTeX 
@inproceedings{yun2024vista,
  title={VisTA-SR: Improving the Accuracy and Resolution of Low-Cost Thermal Imaging Cameras for Agriculture},
  author={Yun, Heesup and Lo, Sassoum and Diepenbrock, Christine H. and Bailey, Brian N. and Earles, J. Mason},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  pages={5470--5479},
  year={2024}
}

Journal Articles

  • J11 Uyehara, I. K., Yun, H., Rizzo, K. T., Ranario, E., & Earles, M. (2026). AgRowStitch: A Leaf-Scale Image Stitching Pipeline for Ground-Based Agricultural Images. Applied Engineering in Agriculture, 42(3), 307-317.
  • J10 Yun, H., Uyehara, I. K., Droutsas, I., Ranario, E., Diepenbrock, C. H., Bailey, B. N., & Earles, J. M. (2026). A vision language model for generating XML-based organ-level plant architecture representations of cowpea from simulated images. Frontiers in Artificial Intelligence, 9, 1844338. doiarXivcodedataproject
  • J9 Mayanja, I. K., Yun, H., & Bailey, B. N. (2026). Automated calibration of stomatal conductance models from thermal imagery by leveraging synthetic images generated from Helios 3D biophysical model simulations. Journal of Experimental Botany, 77(2), 312–329. doi
  • J8 Ranario, E., Mayanja, I., Yun, H., Bailey, B. N., & Earles, J. M. (2026). Thermal Image Segmentation in Weedy Fields via Synthetic RGB-Trained Models and GAN-Based Cross-Modality Alignment. Plant Phenomics, 100214. doi
  • J7 Jeon, Y.-J., Kim, H. S., Lee, T. S., Park, S. H., Yun, H., & Jung, D.-H. (2025). Multimodal Optical Biosensing and 3D-CNN Fusion for Phenotyping Physiological Responses of Basil Under Water Deficit Stress. Agronomy, 16(1), 55. doi
  • J6 Berlingeri, J., Fuentes, A., Ranario, E., Yun, H., Rim, E. Y., Garrett, O., Howard, A., et al. (2025). Integration of crop modeling and sensing into molecular breeding for nutritional quality and stress tolerance. Theoretical and Applied Genetics, 138(9), 205. journal
  • J5 Kim, D.-W., Jeong, S. J., Lee, W. S., Yun, H., Chung, Y. S., Kwon, Y.-S., & Kim, H.-J. (2023). Growth monitoring of field-grown onion and garlic by CIE Lab* color space and region-based crop segmentation of UAV RGB images. Precision Agriculture, 24(5), 1982–2001. journal
  • J4 Yun, H., Kim, H. J., Cho, W., Kim, H. S., & Lim, S. J. (2020). Development of an in situ Dead Leaf Weight Monitoring System. Precision Agriculture Science and Technology, 2(3), 181–188. (in Korean) journal
  • J3 Kim, D. W., Yun, H. S., Jeong, S. J., Kwon, Y. S., Kim, S. G., Lee, W. S., & Kim, H. J. (2018). Modeling and testing of growth status for Chinese cabbage and white radish with UAV-based RGB imagery. Remote Sensing, 10(4), 563. journal
  • J2 Yun, H. S., Park, S. H., Kim, H. J., Lee, W. S., Lee, K. D., Hong, S. Y., & Jung, G. H. (2016). Use of unmanned aerial vehicle for multi-temporal monitoring of soybean vegetation fraction. Journal of Biosystems Engineering, 41(2), 126–137. recordcover Cover article.
  • J1 Lee, K. D., Na, S. I., Baek, S. C., Park, K. D., Choi, J. S., Kim, S. J., Kim, H. J., Yun, H. S., & Hong, S. Y. (2015). Estimating the Amount of Nitrogen in Hairy Vetch on Paddy Fields using Unmanned Aerial Vehicle Imagery. Korean Journal of Soil Science and Fertilizer, 48(5), 384–390. (in Korean) record

Conference Proceedings

  • C4 Kamangir, H., Yun, H., & Earles, J. M. (2026). Time, Space, and Modality: Probing Earth Foundation Models for Intra-Field Crop Yield Forecasting. In 2nd Workshop on Representation Learning for Earth Observation (REO2), NeurIPS 2026. post
  • C3 Lundqvist, L., Ranario, E., Kamangir, H., Yun, H., Diepenbrock, C., Bailey, B. N., & Earles, J. M. (2026). Does Your VFM Speak Plant? The Botanical Grammar of Vision Foundation Models for Object Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Agriculture-Vision Workshop (pp. 9768–9776). pdfarXiv
  • C2 Ranario, E., Lundqvist, L., Yun, H., Bailey, B. N., & Earles, J. M. (2025). AGILE: A Diffusion-Based Attention-Guided Image and Label Translation for Efficient Cross-Domain Plant Trait Identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Agriculture-Vision Workshop (pp. 5392–5401). pdf
  • C1 Yun, H., Lo, S., Diepenbrock, C. H., Bailey, B. N., & Earles, J. M. (2024). VisTA-SR: Improving the Accuracy and Resolution of Low-Cost Thermal Imaging Cameras for Agriculture. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Agriculture-Vision Workshop (pp. 5470–5479). pdfcodedataproject

Preprints

  • P1 Yun, H., Uyehara, I. K., Ranario, E., Lundqvist, L., Diepenbrock, C. H., Bailey, B. N., & Earles, J. M. (2026). Using Vision Language Foundation Models to Generate Plant Simulation Configurations via In-Context Learning. arXiv preprint arXiv:2603.08930. arXivcodedataproject

Conference Presentations

  • 10 Yun, H., Uyehara, I. K., Ranario, E., Lundqvist, L., Diepenbrock, C. H., Bailey, B. N., & Earles, J. M. (2026). Plant simulator meets vision language models: Generating plant simulation configurations via in-context learning. ASABE Annual International Meeting, Indianapolis, IN, Paper No. 2600400. Presents the arXiv:2603.08930 work.
  • 9 Yun, H., Droutsas, I., Bailey, B. N., Diepenbrock, C., & Earles, M. (2024). Closing the Real2Sim gap between real images and simulation models of cowpea. ASABE Annual International Meeting, Anaheim, CA.
  • 8 Yun, H., & Earles, M. (2023). Improving low-cost thermal images for estimating traits of cowpea and common beans. ASABE Annual International Meeting, Omaha, NE.
  • 7 Mayanja, I. K., Yun, H., Earles, M., & Bailey, B. N. (2023). Automated parameterization of stomatal conductance models from thermal imagery by leveraging synthetic images generated from Helios 3D biophysical model simulations. North American Plant Phenotyping Network, West Lafayette, IN.
  • 6 Kim, D., Jeong, S. J., Yun, H., Kwon, Y. S., & Kim, H. J. (2017). Validation testing of UAV-based vegetable growth estimation models. ASABE Annual International Meeting. doi
  • 5 Yun, H., Jung, S. J., & Kim, H. J. (2016). Radiometric calibration and vegetation index analysis of upland-crop UAV images. Proceedings of the Korean Society for Agricultural Machinery Conference, 21(1), 129-130. project
  • 4 Yun, H., Kim, H. J., Park, K., Lee, K., & Hong, S. (2015). Use of an UAV for biomass monitoring of hairy vetch. ASABE Annual International Meeting. doi
  • 3 Yun, H., Cho, W. J., Jiang, J. S., & Kim, H. J. (2015). Estimation of leaf area, plant height, and fresh weight of lettuce in a plant factory using structure from motion (SfM). Proceedings of the Korean Society for Agricultural Machinery Conference, 20(2), 171-172.
  • 2 Yun, H. S., Kim, H. J., Lee, K. D., Hong, S. Y., & Park, K. D. (2015). Monitoring hairy vetch growth using UAV images. Proceedings of the Korean Society for Agricultural Machinery Conference, 20(1), 73-74.
  • 1 Park, S. H., Yun, H. S., Kim, H. J., Lee, K. D., Hong, S. Y., & Jung, G. H. (2014). Multi-temporal visualizing of soybean crop canopy using images from an unmanned aerial vehicle (UAV). Proceedings of the Korean Society for Agricultural Machinery Conference, 19(2), 223.

Other Abstracts and Earlier Versions

  • Ranario, E., Mayanja, I., Yun, H., Bailey, B. N., & Earles, J. M. (2025). Enabling Plant Phenotyping in Weedy Environments using Multi-Modal Imagery via Synthetic and Generated Training Data. arXiv preprint arXiv:2509.19208. arXiv Preprint of the 2026 Plant Phenomics article.
  • Mayanja, I., Yun, H., Earles, M., & Bailey, B. N. (2023). Automated parameterization of stomatal conductance models from thermal imagery by leveraging synthetic images generated from Helios 3D biophysical model simulations. Authorea Preprints. Earlier preprint of the 2026 Journal of Experimental Botany article.
  • Jeong, S., Kim, D., Yun, H., Cho, W., Kwon, Y., & Kim, H. (2017). Monitoring the growth status variability in Onion (Allium cepa) and Garlic (Allium sativum) with RGB and multi-spectral UAV remote sensing imagery. In Proceedings of the 7th Asian-Australasian Conference on Precision Agriculture (pp. 1–8).
  • Jeong, S. J., Yun, H., Kim, D. W., Kim, H. J., Yoo, C. S., & Kwon, Y. S. (2016). Development of an Image Segmentation Method for Monitoring Garlic Growth Status Using UAV Imagery. Proceedings of the Korean Society for Agricultural Machinery Conference, 21(2), 58.
  • Seo, S. H., Yun, H., Jeong, S. J., Kim, D. W., Kim, G. S., & Kim, S. G. (2016). Study on Optimal Image Acquisition Methods for UAV Remote Sensing of Upland Crops. Proceedings of the Korean Society for Agricultural Machinery Conference, 21(2), 56.
  • Yun, H., Lee, I. S., Lee, H. T., Kim, H. S., & Kim, H. J. (2015). Development of a Field Monitoring System for Moisture Change in Fallen Leaves. Proceedings of the Korean Society for Agricultural Machinery Conference, 20(2), 343–344.