Publications

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

1. H. Dakshinamurthy*, S. Jones, S. Corkins*, P. Pandey*, and S. Young†, “Design and evaluation of an aerial vehicle payload for automated, near-surface soil moisture measurements,” Computers and Electronics in Agriculture, 2024. doi.org/10.1016/j.compag.2024.109518.

2. P. Pandey*, J. Acosta, K. Payn, and S. Young†, “Towards autonomous, aerial pollination: Design of a robotic pollinator payload for controlled crosses in loblolly pine,” Applied Engineering in Agriculture, vol. 40, no. 6, 2024. doi.org/10.13031/aea.15916.

3. Nguyen, A.*, Ore, J.P., Castro-Bolinaga, C., Hall, S., Young, S. (2024). Towards autonomous, optimal water sampling with aerial and surface vehicles for rapid water quality assessment. Journal of the ASABE. 67(1): 91-98. doi.org/10.13031/ja.15796

4. Franzluebbers, A. J., van Vliet, S., Young, S.N., & Poore, M. H. (2024). Soil health and root-zone enrichment characteristics between paired grassland and cropland fields in the southeastern United States. Grassland Research, 1–10. doi.org/10.1002/glr2.12066

5. P. Pandey*, P. Veazie*, B. Whipker, and S.N. Young, (2023). Predicting foliar nutrient concentrations and nutrient deficiencies of hydroponic lettuce using hyperspectral imaging, Biosystems Engineering, vol. 226, pp. 458-469, 10.1016/j.biosystemseng.2023.05.005

6. S.N. Young, M. Han, and J. Peschel, (2023). Computer vision approach for tile drain outlet overflow monitoring and flow rate estimation, Applied Engineering in Agriculture, vol. 39, no. 2, pp. 153-165, 10.13031/aea.15157

7. 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.

8. 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.

9.  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.

10. 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. 

11. Veazie, P.*, Pandey, P.*, Young, S.N., Ballance, M.S., Hicks, K., Whipker, B. Impact of Macronutrient Fertility on Mineral Uptake and Growth of Lactuca sativa ‘Salanova Green’ in a Hydroponic System. Horticulturae. 2022; 8(11):1075. doi.org/10.3390/horticulturae8111075

12. 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.

13.  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.

14.  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.

15.  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.

16.  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.

17.  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.

18.  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.

19.  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.

20.  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.

21. 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.

22.  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.

23.  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.

24.  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.

25.  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.

26.  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.

27.  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.

28.  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.

29.  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.

30.  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.

31.  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. M. Esteghamati, M. Ahmed, S. Young, and M. Halling, (2024) “A multi-task deep learning-based framework for structural details and corrosion detection in steel bridges,” in 12th International Conference on Bridge Maintenance, Safety and Management, IABMAS. 

2. R. Jeon, J. Peschel, B. Ramirez, J. Holt, S. Young, and A. Nguyen*, (2023) “Late-finishing pig body weight estimation using extrapolation from side surface point clouds,” in 2nd US Precision Livestock Farming Conference (USPLF), University of Tennessee Institute of Agriculture, 2023.

3. 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.

4. 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.

5.  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.

6.  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.