Advances in genotyping technology and reductions in cost have unraveled the need to invest in high-throughput phenotyping technologies to be able to perform gene discovery tests at large scale. Advantages of high-throughput phenotyping technologies include the opportunity to obtain data that could not be collected by hand, as well as improvements in accuracy and repeatability. This is an interdisciplinary research topic receiving significant attention and funding.

In 2012, Dr. Salas Fernandez was part of a team (with Dr. P. Schnable and Dr. L. Tang) who developed PHENOBOT, a state-of-the-art image-based platform for tall biomass crops to generate large data sets of plant architecture traits during the entire growing season. Advancements in algorithm development for 3D image reconstruction and the automatic extraction of architectural features were the most novel contributions of this research, published in the Breakthrough Technologies section of Plant Physiology. We have also made significant discoveries based on the use of novel image-derived descriptors of plant architecture, associated with biomass yield. We have additionally utilized this novel technology to extract traits at different layers of the canopy, generating novel within-plant detailed data that could not be extracted by hand.

We are also working on the collection, analysis and exploitation of data obtained with indoor HTP technologies using 3D images, we have developed and validated algorithms to extract morphological traits and associated them with growth rates and biomass yield. Finally, we have developed an automatically controlled irrigation system for large drought experiments, in which water content is measured and controlled in each pot independently.

Co-PI: Salas Fernandez, MG.
Funding: USDA AFRI (USDA-DOE Plant Feedstocks Genomics for Bioenergy), Plant Sciences Institute (ISU).

Selected Publications:

• Breitzman MW, Bao Y, Tang L, Schnable PS, Salas-Fernandez MG. (2019). Linkage disequilibrium mapping of high-throughput image-derived descriptors of plant architecture traits under field conditions. Field Crops Research 244, 107619
• Xiang L, Bao Y, Tang L, Ortiz D, M.G. Salas-Fernandez. (2019). Automated morphological traits extraction for sorghum plants via 3D point cloud data analysis. Computers and Electronics in Agriculture 162: 951-961.
• Zhou Y., S. Srinivasan, S.V. Mirnezami, A. Kusmec, Q. Fu, L. Attigala, M.G. Salas Fernandez, B. Ganapathysubramanian, and P.S. Schnable. (2019). Genome-wide association study for sorghum panicle architecture using semi-automated, high-throughput feature extraction from RGB images. Plant Physiology 179 (1): 24-37. DOI: 10.1104/pp.18.00974 .
• Bao Y., L. Tang, M. Breitzman, M.G. Salas Fernandez, and P.S. Schnable. (2019). In-field robotic phenotyping of sorghum plant architecture using stereo vision. J. Field Robotics 35: 397– 415. https://doi.org/10.1002/rob.21830
• Ortiz, D., A. Litvin, and M.G. Salas Fernandez. (2018). A low-cost automated irrigation system for precise high-throughput phenotyping in drought stress studies. PLoS ONE 13 (6): e0198546.  https://doi.org/10.1371/journal.pone.0198546
• Salas Fernandez, M.G., Y. Bao, L. Tang, P.S. Schnable. (2017). A high-throughput field-based phenotyping technology for tall biomass crops. Plant Physiology 174 (4): 2008-2022. DOI: 10.1104/pp.17.00707.