RESEARCH

Peer Reviewed Articles

• 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
• Hirata Sanches, V., Quiñones, R., Vivas, J., Guillaume, J. H., Iwanaga, T., Kwakkel, J. H., … & Lade, S. J. (2025). Integrating diversity and agency into social-ecological resilience metrics. Ecology and society, 30(4).
• Quiñones, R., Banu, S. M., & Gultepe, E. (2025). GCNet: A Deep Learning Framework for Enhanced Grape Cluster Segmentation and Yield Estimation Incorporating Occluded Grape Detection with a Correction Factor for Indoor Experimentation. Journal of Imaging, 11(2), 34. https://doi.org/10.3390/jimaging11020034
• Quiñones, Rubi. “OSC-CO^ 2: 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
• Benjamin Wingerter, Rubi Quiñones, and Dominic Cristiano. August 2023. DA³-BES: Exploring Complex Adaptive Systems Using Dynamic Multi-Agent Models for Honey Bee Colony Environment Simulation. Teaching Issues and Experiments in Ecology, Vol. 19: Experiment #1. https://tiee.esa.org/vol/v19/experiments/wingerter/abstract.html
• Cox, M., Harrison, H., Partelow, S., Curtis, S., Elser, S., Hammond Wagner, C., … R. Quiñones & Whittaker, B. (2023). How academic podcasting can change academia and its relationship with society: A conversation and guide. Frontiers in Communication, 8.
• F. E. Hogan, J. A. Fowler, C. D. Barnes, A. K. Ludwig , D. J. Cristiano, K., D. Morales, R. Quiñones, D. Twidwell, J. M. Dauer. “New multimedia resources for ecological resilience education in modern university classrooms.” Eco-Sphere: Eco-Education Track. October 2022. http://doi.org/10.1002/ecs2.4245
• Quiñones R, Munoz-Arriola F, Choudhury SD, Samal A (2021) Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping. PLoS ONE 16(9): e0257001. https://doi.org/10.1371/journal.pone.0257001
• S. Gampa, R. Quiñones. “Data Driven Techniques for Hyperspectral Image Analysis,” CRC Press, Taylor and Francis, 2020. ISBN: 9781315177304
• Firestone, J.W., Quiñones, R., & Duncan, B.A. (2019). Learning from Users: An Elicitation Study and Taxonomy for Communicating small Unmanned Aerial System States Through Gestures. In HRI 2019 – 14th ACM/IEEE International Conference on Human-Robot Interaction (pp. 163-171). [8673010] (ACM/IEEE International Conference on Human-Robot Interaction; Vol. 2019-March). IEEE Computer society. https://doi.org/10.1109/HR.2019.8673010

Datasets

• 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
  • Please cite: 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., 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
  • Please cite: Quiñones, Rubi. “OSC-CO^ 2: 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, 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
  • Please cite: Quiñones R, Munoz-Arriola F, Choudhury SD, Samal A (2021) Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping. PLoS ONE 16(9): e0257001. https://doi.org/10.1371/journal.pone.0257001

Software

• DA³-BES Bee Simulation Model: https://github.com/benwingerter/bee-simulation/releases
  • Please cite: Benjamin Wingerter, Rubi Quiñones, and Dominic Cristiano. August 2023. DA³-BES: Exploring Complex Adaptive Systems Using Dynamic Multi-Agent Models for Honey Bee Colony Environment Simulation. Teaching Issues and Experiments in Ecology, Vol. 19: Experiment #1. https://tiee.esa.org/vol/v19/experiments/wingerter/abstract.html
• OSC-CO^2: https://github.com/rubiquinones/OSC-CO2
  • Please cite: Quiñones, Rubi. “OSC-CO^2: 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
• HyperRPheno: https://plantvision.unl.edu/software
  • Please cite: S. Gampa, R, Quiñones, “Data-Driven Techniques forPlant Phenotyping Using Hyperspectral Imagery”, Intelligent Image Analysis for Plant Phenotyping, CRC Press, Taylor and Francis Group, First Edition, 2020.

Grants

• $40,000 – NIH STRR Grant for “Customized Infant Breastfeeding Accessories for Breastfeeding Success”
• $16,000 – SIUE’ Seed Grants for Transitional and Exploratory Projects (STEP) Grant for “A Multi-Agent Simulation to Understand the Environmental Influence on Plant Growth”