Sierra Young

Publications | Abstracts

An asterisk (*) at the end of a publication indicates that it has not been peer-reviewed.

Publications | Book Chapters

• Young, S., Pandey, P., Lorence, A., Medina Jimenez, K., (2022). Design Considerations for In-Field Measurement of Plant Architecture Traits Using Ground-Based Platforms: Methods in Molecular Biology. Springer US
• Young, S., (2020). Analyzing Sensor Data at the Source: Case Studies and Modules for Data Science Instruction. American Society of Agricultural and Biological Engineers

An asterisk (*) at the end of a publication indicates that it has not been peer-reviewed.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An asterisk (*) at the end of a publication indicates that it has not been peer-reviewed.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

• Neupane, S., Horsburgh, J.S, Bin Issa, R., Young, S., (2026). HydrocamCollect: A robust data acquisition and cloud data transfer workflow for camera-based hydrological monitoring. Environmental Modelling & Software, 196, 106770. doi: 10.1016/j.envsoft.2025.106770
• Bin Issa, R., Neupane, S., Khan, S., Horsburgh, J.S, Young, S., (2026). Towards Real-Time Water Level and Discharge Measurements using Imagery, Machine Learning, and Edge Computing. Hydroinformatics, doi: 10.2166/hydro.2026.147
• Neupane, S., Horsburgh, J.S, Bin Issa, R., Young, S., (2026). HydrocamCollect: A Robust Data Acquisition and Cloud Data Transfer Workflow for Camera-based Hydrological Monitoring. Environmental Modelling & Software, 196, doi: 10.1016/j.envsoft.2025.106770
• Young, S., (2025). Intelligent Robots for Agriculture --Ag-Robot Development, Navigation, and Information Perception (Editorial). Frontiers in Robotics and AI, 12
• Ahsin, M., Varre, J., Poore, M., Rogers, J., Franzluebbers, A., Young, S., Kronberg, S., Provenza, F., Bain, J., Van Vliet, S., (2025). Improved Soil Health and Pasture Phytochemical Richness Underlies Improved Beef Nutrient Density in Southern US Grass-Finished Beef Systems. npj Science and Food, 9:151
• Sorenson, A., McLean, J.E, Young, S., (2025). Arsenic mobilization at the water-shore interface of a shrinking saline lake. Science of The Total Environment, 999:15, 180318. doi: 10.1016/j.scitotenv.2025.180318
• Safre, A.L, Torres-Rua, A., Black, B.L, Young, S., (2025). Deep learning framework for fruit counting and yield mapping in tart cherry using YOLOv8 and YOLO11. Smart Agricultural Technology, 11, 100948. doi: 10.1016/j.atech.2025.100948
• Dakshinamurthy, H.N, Jones, S.B, Schwartz, R., Young, S., (2025). Waveform analysis for short time domain reflectometry (TDR) probes to obtain calibrated moisture measurements from partial vertical sensor insertions . Computers and Electronics in Agriculture, 235:110233
• Dakshinamurthy, H.N, Jones, S.B, Corkins, S., Pandey, P., Young, S., (2024). Design and evaluation of an aerial vehicle payload for automated, near-surface soil moisture measurements. Computers and Electronics in Agriculture
• 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, 91-98. doi: doi.org/10.13031/ja.15796
• Pandey, P., Acosta, J.J, Payn, K.G, Young, S., (2024). Towards Autonomous, Aerial Pollination: Design of a Robotic Pollinator Payload for Controlled Crosses in Loblolly Pine. Applied Engineering in Agriculture, 40:6, 635-649. doi: 10.13031/aea.15916
• Franzluebbers, A.J, Van Vliet, S., Young, S., Poore, M.H, (2023). Soil health and root‐zone enrichment characteristics between paired grassland and cropland fields in the southeastern United States. Grassland Research, 2:4, 299-308. doi: 10.1002/glr2.12066
• Pandey, P., Veazie, P., Whipker, B., Young, S., (2023). Predicting foliar nutrient concentrations and nutrient deficiencies of hydroponic lettuce using hyperspectral imaging. Biosystems Engineering, 230, 458-469. 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
• 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
• 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
• Veazie, P., Pandey, P., Young, S., Ballance, M.S, Hicks, K., Whipker, B., (2022). Impact of Macronutrient Fertility on Mineral Uptake and Growth of Lactuca sativa ‘Salanova Green’ in a Hydroponic System. Horticulturae, 8:11, 1075. doi: 10.3390/horticulturae8111075
• 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
• Linder, E.R, Young, S., Li, X., Henriquez Inoa, S., Suchoff, D.H, (2022). The Effect of Transplant Date and Plant Spacing on Biomass Production for Floral Hemp (Cannabis sativa L.). Agronomy, 12:8, 1856. doi: 10.3390/agronomy12081856
• Chen, D., Lu, Y., Li, Z., Young, S., (2022). Performance evaluation of deep transfer learning on multi-class identification of common weed species in cotton production systems. Computers and Electronics in Agriculture, 198, 107091. doi: 10.1016/j.compag.2022.107091
• 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
• 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
• 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 L.). 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.
• 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. doi: 10.13031/trans.14708
• 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.
• 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. doi: 10.3390/rs13183595
• 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. doi: 10.1093/forsci/fxab003
• Barnes, E., Morgan, G., Hake, K., Devine, J., Kurtz, R., Ibendahl, G., Sharda, A., Rains, G., Snider, J., Maja, J.M, Thomasson, J.A, Lu, Y., Gharakhani, H., Griffin, J., Kimura, E., Hardin, R., Raper, T., Young, S., Fue, K., Pelletier, M., Wanjura, J., Holt, G., (2021). Opportunities for Robotic Systems and Automation in Cotton Production. AgriEngineering, 3:2, 339-362. doi: 10.3390/agriengineering3020023
• 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. doi: 10.1007/s11119-020-09742-2
• 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.
• Lu, Y., Young, S., (2020). A survey of public datasets for computer vision tasks in precision agriculture. Computers and Electronics in Agriculture, 178, 105760. doi: 10.1016/j.compag.2020.105760
• 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. doi: 10.1002/hyp.13707
• 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. doi: 10.3390/w9070494

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.