Ian Ostermann

iosterma@purdue.edu
LinkedIn

Publications (2)

3D Reconstruction Enables High-Throughput Phenotyping and Quantitative Genetic Analysis of Phyllotaxy (Davis et al. 2025)

Davis, J. M., Gaillard, M., Tross, M. C., Shrestha, N., Ostermann, I., Grove, R. J., Li, B., Benes, B., & Schnable, J. C. (2025). 3D Reconstruction Enables High-Throughput Phenotyping and Quantitative Genetic Analysis of Phyllotaxy. Plant Phenomics, 100023. https://doi.org/https://doi.org/10.1016/j.plaphe.2025.100023

@article{Davis25PlPh, author = {Davis, Jensina M. and Gaillard, Mathieu and Tross, Michael C. and Shrestha, Nikee and Ostermann, Ian and Grove, Ryleigh J. and Li, Bosheng and Benes, Bedrich and Schnable, James C.}, title = {3D Reconstruction Enables High-Throughput Phenotyping and Quantitative Genetic Analysis of Phyllotaxy}, journal = {Plant Phenomics}, pages = {100023}, year = {2025}, issn = {2643-6515}, doi = {https://doi.org/10.1016/j.plaphe.2025.100023}, image = {Davis-Phenomics-2025-3DRecon.png}, pdf = {Davis-Phenomics-2025-3DRecon.pdf} }

Sorghum segmentation and leaf counting using in silico trained deep neural model (Ostermann et al. 2024)

Ostermann, I., Benes, B., Gaillard, M., Li, B., Davis, J., Grove, R., Shrestha, N., Tross, M. C., & Schnable, J. C. (2024). Sorghum segmentation and leaf counting using in silico trained deep neural model. The Plant Phenome Journal, 7(1), e70002.

@article{Ostermann24PlantPhenome, title = {Sorghum segmentation and leaf counting using in silico trained deep neural model}, author = {Ostermann, Ian and Benes, Bedrich and Gaillard, Mathieu and Li, Bosheng and Davis, Jensina and Grove, Ryleigh and Shrestha, Nikee and Tross, Michael C and Schnable, James C}, journal = {The Plant Phenome Journal}, volume = {7}, number = {1}, pages = {e70002}, year = {2024}, publisher = {Wiley Online Library}, image = {Ostermann-PPJ-2024-Sorghum.jpg}, pdf = {Ostermann-PPJ-2024-Sorghum.pdf} }