Publications

Please see Google Scholar for a complete, up-to-date list of publications

*,** denotes work conducted with graduate/undergraduate students and postdocs, respectively

  1. A. H. Nguyen*, J. P. Holt, M. T. Knauer, V. A. Abner*, E. J. Lobaton, and S. N. Young, (2023). Towards rapid weight assessment of finishing pigs using a handheld, mobile RGB-D camera, Biosystems Engineering, vol. 226, pp. 155–168, 10.1016/j.biosystemseng.2023.01.005.

  2. Y. Lu**, X. Li, S.N. Young, X. Li, E. Linder, D. Suchoff (2022). Hyperspectral imaging with chemometrics for non-destructive determination of cannabinoids in floral and leaf materials of industrial hemp (Cannabis sativa L.). Computers and Electronics in Agriculture, 178, 105760, doi.org/10.1016/j.compag.2022.107387.

  3. E. Linder, S.N. Young, X. Li, S. Henriquez-Inoa, D. Suchoff (2022). The Effect of Harvest Date on Temporal Cannabinoid and Biomass Production in the Floral Hemp (Cannabis sativa L.) Cultivars BaOx and Cherry Wine. Horticulturae, 8(10), 959, doi.org/10.3390/horticulturae8100959.

  4. Lu, Y.**, Young, S.N., Linder, E., Whipker, B., Suchoff, D., (2022). Hyperspectral imaging with machine learning to differentiate cultivars, growth stages, flowers and leaves of industrial hemp (Cannabis sativa). Frontiers in Plant Science, 12. doi.org/10.3389/fpls.2021.810113.

  5. E. Linder, S.N. Young, X. Li, S. Henriquez-Inoa, D. Suchoff (2022). The Effect of Transplant Date and Plant Spacing on Biomass Production for Floral Hemp (Cannabis sativa L.). Agronomy, 1856. doi.org/10.3390/agronomy12081856.

  6. D. Chen, Y. Lu**, Z. Li, S.N. Young (2022). Performance Evaluation of Deep Transfer Learning on Multiclass Identification of Common Weed Species in Cotton Production Systems. Computers and Electronics in Agriculture, 198, 107091, doi.org/10.1016/j.compag.2022.107091.

  7. Y. Lu**, S.N. Young, H. Wang, N. Wijewardane (2022). Robust plant segmentation of color images based on image contrast optimization. Computers and Electronics in Agriculture, 193, 106711. doi.org/10.1016/j.compag.2022.106711.

  8. S. Kendler, R. Aharoni, S.N. Young, H. Sela, T. Kis-Papo, T. Fahima, B. Fishbain (2022). Detection of crop diseases using enhanced variability imagery data and convolutional neural networks. Computers and Electronics in Agriculture, 193, 106732. doi.org/10.1016/j.compag.2022.106732.

  9. S.M. Saia, N.G. Nelson, S.N. Young, S. Parham, M. Vandegrift (2022). Ten simple rules for researchers who want to develop web apps. PLoS Comput Biol 18(1): e1009663. doi.org/10.1371/journal.pcbi.1009663.

  10. S.N. Young, R. Lanciloti*, J. Peschel (2022). The Effects of Interface Views on Performing Aerial Telemanipulation Tasks using Small UAVs. International Journal of Social Robotics. 14, 213-228, doi.org/10.1007/s12369-021-00783-9.

  11. Y. Lu**, K. Payn, P. Pandey*, J. Acosta, A. Heine, T. Walker, S.N. Young (2021). Hyperspectral imaging with cost-sensitive learning for high-throughput screening of loblolly pine (Pinus taeda L.) seedlings for freeze tolerance. Transactions of the ASABE, 64(6):2045:2059. doi.org/10.13031/trans.14708.

  12. P. Pandey*, K. Payn, Y. Lu**, A. Heine, T. Walker, J. J. Acosta, S.N. Young (2021). Hyperspectral Imaging Combined with Machine Learning for the Detection of Fusiform Rust Disease Incidence in Loblolly Pine Seedlings. Remote Sensing, 13(18), 3595. doi.org/10.3390/rs13183595.

  13. S. Kronberg, F. Provenza, S. van Vliet, S.N. Young (2021). Review: Closing nutrient cycles for animal production – Current and future agroecological and socio-economic issues. Animal, 15(1), 100285. doi.org/10.1016/j.animal.2021.100285.

  14. E. Barnes, G. Morgan, K. Hake, J. Devine, R. Kurtz, G. Ibendahl, A. Sharda, G. Rains, J. Snider, J. M. Maja, J. A. Thomasson, Y. Lu, H. Gharakhani, J. Griffin, E. Kimura, R. Hardin, T. Raper, S.N. Young, K. Fue, M. Pelletier, J. Wanjura, and G. Holt (2021). Opportunities for Robotic Systems and Automation in Cotton Production. AgriEngineering, 3(2), 339-362. doi.org/10.3390/agriengineering3020023.

  15. P. Pandey*, Hemanth Narayan D.*, S.N. Young (2021). Autonomy in detection, actuation, and planning for robotic weeding systems. Transactions of the ASABE, 64(2): 557-563. doi.org/10.13031/trans.14085.

  16. Y. Lu**, T. Walker, K. Payn, J. Acosta, S.N. Young, P. Pandey*, A. Heine (2021). Prediction of freeze damage and minimum winter temperature of the seed source of loblolly pine seedlings using hyperspectral imaging. Forest Science 67(3), 321–334. doi:10.1093/forsci/fxab003.

  17. Y. Lu**, S.N. Young (2020). A survey of public datasets for computer vision tasks in precision agriculture. Computers and Electronics in Agriculture, 178, 105760, doi.org/10.1016/j.compag.2020.105760.

  18. R. Aharoni, A. Klymiuk, B. Sarusi, S.N. Young, T. Fahima, B. Fishbain, S. Kendler (2020). Spectral light-reflection data dimensionality reduction for timely detection of yellow rust. Precision Agriculture, doi.org/10.1007/s11119-020-09742-2.

  19. G. Penny, V. Srinivasan, R. Apoorva, K. Jeremiah, J.M. Peschel, S.N. Young, S. Thompson (2020). A process-based approach to attribution of historical streamflow decline in a data‐scarce and human-dominated watershed. Hydrological Processes, doi.org/10.1002/hyp.13707.

  20. S.N. Young, J. Peschel (2020). Review of Human-Machine Interfaces for Small Unmanned Systems with Robotic Manipulators. IEEE Transactions on Human Machine Systems. doi:10.1109/THMS.2020.2969380.

  21. S.N. Young, J. Peschel, E. Kayacan (2019). Design and Field Evaluation of a Ground Robot for High-Throughput Phenotyping of Energy Sorghum. Precision Agriculture. doi.org/10.1007/s11119-018-9601-6.

  22. S.N. Young (2019). A Framework for Evaluating Field-Based, High-Throughput Phenotyping Systems: A Meta-Analysis. Sensors, 19, 3582 doi.org/10.3390/s19163582.

  23. E. Kayacan, S.N. Young, J. Peschel, G. Chowdhary (2018). High Precision Control of Tracked Field Robots in the Presence of Unknown Traction Coefficients. J. Field Robotics. doi.org/10.1002/rob.21794.

  24. S.N. Young, J.M. Peschel, G. Penny, S. Thompson, V. Srinivasan (2017). Robot-Assisted Measurement for Hydrologic Understanding in Data Sparse Regions. Water, 9(7) doi.org/10.3390/w9070494.

Conference Proceedings

  1. P. Pandey*, J.J. Acosta, K.G. Payn, S.N. Young (2022). Towards aerial robotic pollination for controlled crosses in Pinus taeda. 2022 ASABE Annual International Meeting, 2200677. doi:10.13031/aim.202200677.

  2. S.G. Hall, M.D. Campbell, V.M. Campbell, A. Geddie, M.O. Frinsko, M. Greensword, R. Hasan, N. Kasera, C. Malveaux, D. Paul, M.T.homas, D. Smith, R. Smith, S.N. Young (2021). Smart Systems to Enhance Sustainability and Add Value to Marine Aquaculture. 2021 ASABE Annual International Virtual Meeting, 2100523. doi:10.13031/aim.202100523.

  3. Y. Lu**, K.G. Payn, P. Pandey*, J.J. Acosta, A.J. Heine, T.D. Walker, S.N. Young (2020). Hyperspectral imaging-enabled high-throughput screening of loblolly pine (Pinus taeda) seedlings for freeze tolerance. 2020 ASABE Annual International Virtual Meeting, 202001072. doi:10.13031/aim.202001072.

  4. P. Pandey*, K.G. Payn, Y. Lu**, A.J. Heine, T.D. Walker, S.N. Young (2020). High Throughput Phenotyping for Fusiform Rust Disease Resistance in Loblolly Pine Using Hyperspectral Imaging. 2020 ASABE Annual International Virtual Meeting, 2000872. doi:10.13031/aim.202000872.

Book Chapters

  1. Young, S., Pandey, P.* (2022). Design Considerations for In-Field Measurement of Plant Architecture Traits Using Ground-Based Platforms: High-Throughput Plant Phenotyping: Methods and Protocols. Springer Nature. Eds.: Lorence, A., Medina Jimenez, K. doi: 10.1007/978-1-0716-2537-8_15.

  2. Young, S., (2020). Analyzing Sensor Data at the Source: Case Studies and Modules for Data Science Instruction. American Society of Agricultural and Biological Engineers

Magazine Articles

  1. Young, S., Ore, J. P., & Hall, S. (2022). The Coming Wave of Aquatic Robotics. Resource Magazine, 29(4), 12-13.