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An asterisk (*) at the end of a publication indicates that it has not been peer-reviewed.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

An asterisk (*) at the end of a publication indicates that it has not been peer-reviewed.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An asterisk (*) at the end of a publication indicates that it has not been peer-reviewed.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