| The application of machine-learning and Raman spectroscopy for the rapid detection of edible oils type and adulteration H Zhao, Y Zhan, Z Xu, JJ Nduwamungu, Y Zhou, R Powers, C Xu Food chemistry 373, 131471, 2022 | 44 | 2022 |
| Mapping infield variability of soil properties to support precision agriculture using UAV, multi-depth EC, and aerial hyperspectral imagery NK Wijewardane, L Wang, Y Zhan, T Franz, H Yu, Y Zhou, Y Shi, Y Ge PSS 2019, 15, 2019 | 3 | 2019 |
| Rapid online plant leaf area change detection with high-throughput plant image data Y Zhan, R Zhang, Y Zhou, V Stoerger, J Hiller, T Awada, Y Ge Journal of Applied Statistics 50 (14), 2984-2998, 2023 | 1 | 2023 |
| Machine learning-driven Raman spectroscopy for rapidly detecting type, adulteration, and oxidation of edible oils H Zhao, Y Zhan, Z Xu, JJ Nduwamungu, Y Zhou, C Xu INFORM 31 (4), 12-15, 2020 | 1 | 2020 |
| Novel Statistical Methods on High-Throughput Plant Phenotyping: Learning, Detection and Efficiency Y Zhan The University of Nebraska-Lincoln, 2021 | | 2021 |
| Machine-Learning-Driven Raman Spectroscopy for Rapidly Detecting Type and Adulteration of Edible Oils. H Zhao, Y Zhan, X Zheng, JJ Nduwamungu, Y Zhou, C Xu JOURNAL OF THE AMERICAN OIL CHEMISTS SOCIETY 97, 13-14, 2020 | | 2020 |
| kZS Journal of Applied Statistics Y Zhan, R Zhang, Y Zhou, V Stoerger, J Hiller, T Awada, Y Ge | | |
