My Dissertation

As the global population is projected to exceed 9 billion by 2050 and crop production is growing at just 1.3% annually amid climate change, developing crops that can withstand stress environments is crucial for food security. Testing crop resilience requires fast, efficient, and accurate data collection, which can be achieved through high-throughput imaging systems and advanced segmentation techniques.

First, I defend the novel integration of co-segmentation (2008) and plant phenotyping by performing a performance study against state-of-the-art segmentation methods. Link to work. I also provide an open-sourced multi-dimensional dataset, CosegPP.

Second, I advance co-segmentation by leveraging the latest state-of-the-art algorithms to create a framework that increases accuracy in plant images by 3% to 45%. Link to work. My novel framework can handle high-dimensional datasets, is end-to-end trainable, and is unsupervised. Our code is open-sourced, and our novel CosegPP+ dataset is open-sourced.

Third, I introduced concurrent imaging and unidimensional phenotypes that are capable of capturing dynamic morphological and physiological responses that are not observable with conventional single-image or cumulative metrics. Link to work. I also provide two open-sourced datasets, SIMID and SIPID.

These are images of an automated, high-throughput plant phenotyping platform (LemnaTec Scanalyzer 3D) at the University of Nebraska-Lincoln. For more information on the system (and the source of the images) visit: https://ard.unl.edu/phenotyping/nebraska-innovation-campus-greenhouse

Citations

If you use any of my dissertation work, please cite the appropriate citations:

  • Dissertation
    • Quiñones, R. (2022). Unsupervised Cosegmentation and Phenotypes for Multi-Modal,-View, and-State Imagery (Doctoral dissertation, The University of Nebraska-Lincoln).
  • Chapter 3
    • Quiñones R, Muñoz-Arriola F, Das Choudhury S, Samal A. Introducing Concurrent Imaging and Unidimensional Analytics for Plant Stress Responses. Plants. 2026; 15(3):428. https://doi.org/10.3390/plants15030428
    • Quiñones, R., Munoz-Arriola, F., Das Choudhury, S., & Samal, A. (2025). Unidimensional Phenotypes using Concurrent Imaging Collected Via a High-Throughput Imaging System [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17400167
  • Chapter 2
    • Quiñones, Rubi. “OSC-CO2: Coattention and Cosegmentation Framework for Plant State Change with Multiple Features.” Frontiers in Plant Science 14: 1211409.https://doi.org/10.3389/fpls.2023.1211409
    • Quiñones, R., Samal, A., Das Choudhury, S., & Munoz-Arriola, F. (2022). Cosegmentation for Plant Phenotyping+ (CosegPP+) Data Repository Collected Via a High-Throughput Imaging System [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6863013
  • Chapter 1
    • Quiñones, R., Munoz-Arriola, F., Choudhury, S. D., & Samal, A. (2021). Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping. Plos one, 16(9), e0257001.
    • Quiñones, Rubi, Munoz Arriola, Francisco, Das Choudhury, Sruti, & Samal. Ashok. (2021). Cosegmentation for Plant Phenotyping (CosegPP) Data Repository Collected Via a High-Throughput Imaging System [Data set]. In Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping. Zenodo. https://doi.org/10.5281/zenodo.5117176

Grant Information

This dissertation is based upon work supported by the National Science Foundation under Grant No. DGE-1735362. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Also, the authors acknowledge the support provided by the Agriculture and Food Research Initiative Grant number NEB-21-176 and NEB-21-166 from the USDA National Institute of Food and Agriculture, Plant Health and Production and Plant Products: Plant Breeding for Agricultural Production.