Sierra Young
Water
Assistant Professor
Contact Information
Office Location: ENGR 228, UWRL 240Phone: 435-797-1519
Email: sierra.young@usu.edu
Additional Information:
Educational Background
Biography
Dr. Sierra Young is an Assistant Professor in the Utah Water Research Laboratory and Civil and Environmental Engineering Department at Utah State University. She received her Ph.D. in Civil Engineering as a Department of Defense Fellow from the University of Illinois in 2018.
Teaching Interests
Dr. Young teaches courses on applied computer vision for civil and environmental engineers and introductory programming.
Research Interests
Broadly speaking, Dr. Young's research focuses on the development of field robotics, automation, and sensing systems for environmental and agricultural applications, and advancing manipulation for unoccupied aerial systems (UAS).
Awards
ASABE Outstanding Reviewer, 2023
American Society of Agricultural and Biological Engineers
Publications | Abstracts
An asterisk (*) at the end of a publication indicates that it has not been peer-reviewed.
- Young, S., Pandey, P., Lorence, A., Medina Jimenez, K., (2022). Design Considerations for In-Field Measurement of Plant Architecture Traits Using Ground-Based Platforms: High-Throughput Plant Phenotyping: Methods and Protocols. Springer Nature
- Young, S., (2020). Analyzing Sensor Data at the Source: Case Studies and Modules for Data Science Instruction. American Society of Agricultural and Biological Engineers
Publications | Book Chapters
An asterisk (*) at the end of a publication indicates that it has not been peer-reviewed.
Publications | Fact Sheets
An asterisk (*) at the end of a publication indicates that it has not been peer-reviewed.
Publications | Journal Articles
Academic Journal
- Nguyen, A., Ore, J., 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, doi: doi.org/10.13031/ja.15796
- Franzluebbers, A., Young, S., Poore, M., (2024). Soil health and root-zone enrichment characteristics between paired grassland and cropland fields in the southeastern USA. Grassland Research, 1-10. doi: 10.1002/glr2.12066
- Pandey, P., Veazie, P., Whipker, B., Young, S., (2023). Predicting Macronutrient Profiles of Hydroponic Lettuce using Hyperspectral Imaging. Biosystems Engineering, 226, 155-168. doi: 10.1016/j.biosystemseng.2023.05.005
- Young, S., Han, M., Peschel, J., (2023). Computer Vision Approach for Tile Drain Outflow Monitoring and Flow Rate Estimation. Applied Engineering in Agriculture, 39:2, doi: 10.13031/aea.15157
- Nguyen, A., Holt, J., Knauer, M., Abner, V., Lobaton, E., Young, S., (2023). Towards Rapid Weight Assessment of Finishing Pigs using a Handheld, Mobile RGB-D Camera. Biosystems Engineering, 226, 155-168. doi: 10.1016/j.biosystemseng.2023.01.005
- Lu, Y., Young, S., Wang, H., Wijewardane, N., (2022). Robust plant segmentation of color images based on image contrast optimization. Computers and Electronics in Agriculture, 193, 106711. doi: 10.1016/j.compag.2022.106711
- Saia, S.M, Nelson, N.G, Young, S., Parham, S., Vandegrift, M., (2022). Ten simple rules for researchers who want to develop web apps. PLoS computational biology, 18:1, e1009663. doi: 10.1371/journal.pcbi.1009663
- Linder, E., Young, S., Li, X., Henriquez Inoa, S., Suchoff, D., (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: 10.3390/horticulturae8100959
- Lu, Y., Li, X., Young, S., Li, X., Linder, E., Suchoff, D., (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, 202, 107387. doi: 10.1016/j.compag.2022.107387
- Linder, E., Young, S., Henriquez-Inoa, S., Li, X., Shuchoff, D., (2022). The effect of transplant date and plant spacing on biomass production for floral hemp (Cannabis sativa L.). Agronomy, 12:8, doi: 10.3390/agronomy12081856
- Kendler, S., Aharoni, R., Young, S., Sela, H., Kis-Papo, T., Fahima, T., Fishbain, B., (2022). Detection of crop diseases using enhanced variability imagery data and convolutional neural networks. Computers and Electronics in Agriculture, 193, 106732. doi: 10.1016/j.compag.2022.106732
- Chen, D., Lu, Y., Li, Z., Young, S., (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: 10.1016/j.compag.2022.107091
- Lu, Y., Young, S., 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: 10.3389/fpls.2021.810113
- Kronberg, S., Provenza, F., van Vliet, S., Young, S., (2021). Closing nutrient cycles for animal production–Current and future agroecological and socio-economic issues. Animal, 15:100285
- Pandey, P., Dakshinamurthy, H.N, Young, S., (2021). Autonomy in Detection, Actuation, and Planning for Robotic Weeding Systems. Transactions of the ASABE, 64:2, 557-563.
- Pandey, P., Payn, K.G, Lu, Y., Heine, A.J, Walker, T.D, Acosta, J.J, Young, S., (2021). Hyperspectral imaging combined with machine learning for the detection of fusiform rust disease incidence in loblolly pine seedlings. Remote Sensing, 13:18, 3595.
- Lu, Y., Payn, K.G, Pandey, P., Acosta, J.J, Heine, A.J, Walker, T.D, Young, S., (2021). Hyperspectral Imaging with Cost-Sensitive Learning for High-Throughput Screening of Loblolly Pine (Pinus taeda L.) Seedling for Freeze Tolerance. Transactions of the ASABE, 64:6, 2045-2059.
- Barnes, E., Morgan, G., Hake, K., Devine, J., Kurtz, R., Ibendahl, G., Sharda, A., Rains, G., Snider, J., Maja, J.M, Thomasson, A., Lu, Y., Gharakhani, H., Griffin, J., Kimura, E., Hardin, R., Raper, T., Young, S., Fue, , Pelletier, M., Wanjura, J., Holt, G., (2021). Opportunities for robotic systems and automation in cotton production. AgriEngineering, 3:2, 339-362.
- Lu, Y., Walker, T.D, Acosta, J.J, Young, S., Pandey, P., Heine, A.J, Payn, K.G, (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.
- Aharoni, R., Klymiuk, V., Sarusi, B., Young, S., Fahima, T., Fishbain, B., Kendler, S., (2021). Spectral light-reflection data dimensionality reduction for timely detection of yellow rust. Precision Agriculture, 22:1, 267-286.
- Young, S., Lanciloti, R.J, Peschel, J.M, (2021). The effects of interface views on performing aerial telemanipulation tasks using small UAVs. International Journal of Social Robotics, 14, 213–228.
- Penny, G., Srinivasan, V., Apoorva, R., Jeremiah, K., Peschel, J., Young, S., Thompson, S., (2020). A process-based approach to attribution of historical streamflow decline in a data-scarce and human-dominated watershed. Hydrological Processes, 34:8, 1981-1995.
- Lu, Y., Young, S., (2020). A survey of public datasets for computer vision tasks in precision agriculture. Computers and Electronics in Agriculture, 178, 105760.
- Young, S., Peschel, J.M, (2020). Review of human--machine interfaces for small unmanned systems with robotic manipulators. IEEE Transactions on Human-Machine Systems, 50:2, 131-143.
- Young, S., (2019). A framework for evaluating field-based, high-throughput phenotyping systems: a meta-analysis. Sensors, 19:16, 3582.
- Young, S., Kayacan, E., Peschel, J.M, (2019). Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum. Precision Agriculture, 20:4, 697-722.
- Kayacan, E., Young, S., Peschel, J.M, Chowdhary, G., (2018). High-precision control of tracked field robots in the presence of unknown traction coefficients. Journal of Field Robotics, 35:7, 1050-1062.
- Young, S., Peschel, J., Penny, G., Thompson, S., Srinivasan, V., (2017). Robot-assisted measurement for hydrologic understanding in data sparse regions. Water, 9:7, 494.
An asterisk (*) at the end of a publication indicates that it has not been peer-reviewed.
Publications | Other
Magazine/Trade Publications
- Young, S., (2022). The Coming Wave of Aquatic Robotics. ASABE Resource Magazine Special Issue: Digital Water *
An asterisk (*) at the end of a publication indicates that it has not been peer-reviewed.