Research

The world population is growing and climate is changing. Agricultural systems are challenged to scale and adapt to meet the increasing demands for food, feed, fiber, and fuel produced on shrinking arable land and with diminishing water supplies.

In the Predictive Plant Phenomics (P3) Program, we have the right people and the right physical and cultural environment necessary to foster the interdisciplinary research needed to solve these looming problems. Groups of plant biologists, engineers, and computational scientists are performing research focused in predictive phenomics, looking at massive amounts of data to create systems and models to predict plant yield. Iowa State engineers are designing robots, sensors, and other advanced equipment that monitor plants in the field and the lab. Data scientists are crunching and analyzing the data collected. These collaborations are developing the agricultural products and computational solutions to meet the emerging needs.

 


 

P3 students and faculty share their research through many avenues: conferences held on campus, national association conferences, international association conferences, symposia, and through peer-reviewed journal articles and other publications.

Here's a selection of recent talks and articles from P3 students and faculty (highlighted in bold):

 

  • Carolyn J Lawrence-DillPatrick S. Schnable, and N. M. Springer. 2019. Idea Factory: the Maize Genomes to Fields Initiative. Crop Science. 59:1406-1410. doi:10.2135/cropsci2019.02.0071
  • Yin Bao, Lie Tang, Srikant Srinivasan, Patrick S Schnable (2019) Field-based architectural traits characterisation of maize plant using time-of-flight 3D imaging. Biosystems Engineering, 178:86-101; doi:10.1016/j.biosystemseng.2018.11.005
  • Yan Zhou, Srikant Srinivasan, Sayed Vahid Mirnezami, Aaron Kusmec, Qi Fu, Lakshmi Attigala, Maria G Salas FernandezBaskar GanapathysubramanianPatrick S Schnable (2019) Semiautomated feature extraction from RGB images for sorghum panicle architecture GWAS. Plant Physiology, 179(1): 24-37; doi:10.1104/pp.18.00974
  • Arti Singh, "Machine learning approaches for automated plant stress phenotyping." 5th International Plant Phenotyping Symposium, Adelaide, Australia. 2-5 October 2018 [click here to view a short clip of her talk]
  • Clayton Carley, "The Soybean Nodule Acquisition Program: Solving a Phenomics Challenge in a SNAP using Machine Learning Solutions." 5th International Plant Phenotyping Symposium, Adelaide, Australia. October 2-5, 2018 [click here to view a short clip of his talk]
  • Patrick Schnable, “The Potential of Predictive Plant Phenotyping to Address (some of) the Challenges Facing Crop Production.”  International Maize and Wheat Improvement Center (CIMMYT). Mexico. 6 September 2018
  • Ian Braun, J Balhoff, TZ Berardini, L Cooper, G Gkoutos, L Harper, E Huala, P Jaiswal, T Kazic, H Lapp, JA Macklin, CD Specht, T Vision, RL Walls, CJ Lawrence-Dill (2018). 'Computable' phenotypes enable comparative and predictive phenomics among plant species across domains of life. Application of Semantic Technologies in Biodiversity Science. Studies on the Semantic Web 33. Thessen, AE. IOS Press/AKA Verlag. ISBN: 978-1-61499-853-2.
  • Carolyn J. Lawrence-Dill, Ted J Heindel, Patrick S Schnable, SJ Strong, J Wittrock, ME Losch, Julie A Dickerson (2018). Transdisciplinary Graduate Training in Predictive Plant Phenomics.  Agronomy. 8 (5), 73. DOI: 10.3390/agronomy8050073
  • Samantha Snodgrass, Matthew B Hufford (2018). Domestication Genomics: untangling the complex history of African rice. Current Biology. 28 (14), 2274. DOI: https://doi.org/10.1016/j.cub.2018.05.072
  • James P McNellie, J Chen, X Li & Jianming Yu. (2018). Genetic Mapping of Foliar and Tassel Heat Stress Tolerance in Maize. Crop Science, 58(6), 2484-2493.

 


 

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