Since human population is expected to grow to more than 9 billion people by 2050, and the average rate of crop production is only increasing 1.3% annually while battling climate change, it is imperative that crops become resilient to stress environments to maintain food supply. To test crop resilience, data collection must be fast, efficient, and accurate. To achieve the data acquisition expectations, high-throughput imaging systems and advanced segmentation techniques are needed.
First, I defend the novel integration of co-segmentation (2008) and plant phenotyping by performing a performance study against traditional segmentation methods. Link to work. I also provide an open-sourced multi-dimensional dataset, CosegPP.
Second, I advance co-segmentation by leveraging the top 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 design phenotypes that span multiple dimensions in a plant image sequence dataset that allows more information about the evolution of the plant. Link to work.
Citations
If you use any of my dissertation work, please cite the appropriate citations:
- Quiñones, R. (2022). Unsupervised Cosegmentation and Phenotypes for Multi-Modal,-View, and-State Imagery (Doctoral dissertation, The University of Nebraska-Lincoln).
- 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
- 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.