| A survey of public datasets for computer vision tasks in precision agriculture Y Lu, S Young Computers and Electronics in Agriculture 178, 105760, 2020 | 240 | 2020 |
| Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum SN Young, E Kayacan, JM Peschel Precision Agriculture 20 (4), 697-722, 2019 | 104 | 2019 |
| Performance evaluation of deep transfer learning on multi-class identification of common weed species in cotton production systems D Chen, Y Lu, Z Li, S Young Computers and Electronics in Agriculture 198, 107091, 2022 | 78 | 2022 |
| High‐precision control of tracked field robots in the presence of unknown traction coefficients E Kayacan, SN Young, JM Peschel, G Chowdhary Journal of Field Robotics 35 (7), 1050-1062, 2018 | 41 | 2018 |
| Review of human–machine interfaces for small unmanned systems with robotic manipulators SN Young, JM Peschel IEEE Transactions on Human-Machine Systems 50 (2), 131-143, 2020 | 39 | 2020 |
| Robust plant segmentation of color images based on image contrast optimization Y Lu, S Young, H Wang, N Wijewardane Computers and Electronics in Agriculture 193, 106711, 2022 | 27 | 2022 |
| Detection of crop diseases using enhanced variability imagery data and convolutional neural networks S Kendler, R Aharoni, S Young, H Sela, T Kis-Papo, T Fahima, B Fishbain Computers and Electronics in Agriculture 193, 106732, 2022 | 26 | 2022 |
| Opportunities for robotic systems and automation in cotton production E Barnes, G Morgan, K Hake, J Devine, R Kurtz, G Ibendahl, A Sharda, ... AgriEngineering 3 (2), 339-362, 2021 | 25 | 2021 |
| Robot-assisted measurement for hydrologic understanding in data sparse regions S Young, J Peschel, G Penny, S Thompson, V Srinivasan Water 9 (7), 494, 2017 | 23 | 2017 |
| Hyperspectral Imaging With Machine Learning to Differentiate Cultivars, Growth Stages, Flowers, and Leaves of Industrial Hemp (Cannabis sativa L.) Y Lu, S Young, E Linder, B Whipker, D Suchoff Frontiers in Plant Science 12, 810113, 2022 | 17 | 2022 |
| Hyperspectral imaging combined with machine learning for the detection of fusiform rust disease incidence in loblolly pine seedlings P Pandey, KG Payn, Y Lu, AJ Heine, TD Walker, JJ Acosta, S Young Remote sensing 13 (18), 3595, 2021 | 16 | 2021 |
| Closing nutrient cycles for animal production–Current and future agroecological and socio-economic issues SL Kronberg, FD Provenza, S Van Vliet, SN Young Animal 15, 100285, 2021 | 15 | 2021 |
| Autonomy in Detection, Actuation, and Planning for Robotic Weeding Systems P Pandey, HN Dakshinamurthy, SN Young Transactions of the ASABE 64 (2), 557-563, 2021 | 15 | 2021 |
| A process‐based approach to attribution of historical streamflow decline in a data‐scarce and human‐dominated watershed G Penny, V Srinivasan, R Apoorva, K Jeremiah, J Peschel, S Young, ... Hydrological processes 34 (8), 1981-1995, 2020 | 11 | 2020 |
| Hyperspectral imaging with chemometrics for non-destructive determination of cannabinoids in floral and leaf materials of industrial hemp (Cannabis sativa L.) Y Lu, X Li, S Young, X Li, E Linder, D Suchoff Computers and Electronics in Agriculture 202, 107387, 2022 | 9 | 2022 |
| Prediction of freeze damage and minimum winter temperature of the seed source of loblolly pine seedlings using hyperspectral imaging Y Lu, TD Walker, JJ Acosta, S Young, P Pandey, AJ Heine, KG Payn Forest Science 67 (3), 321-334, 2021 | 9 | 2021 |
| Ten simple rules for researchers who want to develop web apps SM Saia, NG Nelson, SN Young, S Parham, M Vandegrift PLoS Computational Biology 18 (1), e1009663, 2022 | 8 | 2022 |
| Spectral light-reflection data dimensionality reduction for timely detection of yellow rust R Aharoni, V Klymiuk, B Sarusi, S Young, T Fahima, B Fishbain, ... Precision Agriculture 22, 267-286, 2021 | 8 | 2021 |
| Towards rapid weight assessment of finishing pigs using a handheld, mobile RGB-D camera AH Nguyen, JP Holt, MT Knauer, VA Abner, EJ Lobaton, SN Young biosystems engineering 226, 155-168, 2023 | 7 | 2023 |
| Hyperspectral Imaging with Cost-Sensitive Learning for High-Throughput Screening of Loblolly Pine (Pinus taeda L.) Seedlings for Freeze Tolerance Y Lu, KG Payn, P Pandey, JJ Acosta, AJ Heine, TD Walker, S Young Transactions of the ASABE 64 (6), 2045-2059, 2021 | 6 | 2021 |
Yuzhen LuAssistant Professor, Michigan State UniversityVerified email at msu.edu
