| Comparison of object detection and patch-based classification deep learning models on mid-to late-season weed detection in UAV imagery AN Veeranampalayam Sivakumar, J Li, S Scott, E Psota, A J. Jhala, ... Remote Sensing 12 (13), 2136, 2020 | 155 | 2020 |
| Wheat height estimation using LiDAR in comparison to ultrasonic sensor and UAS W Yuan, J Li, M Bhatta, Y Shi, PS Baenziger, Y Ge Sensors 18 (11), 3731, 2018 | 116 | 2018 |
| Elucidating sorghum biomass, nitrogen and chlorophyll contents with spectral and morphological traits derived from unmanned aircraft system J Li, Y Shi, AN Veeranampalayam-Sivakumar, DP Schachtman Frontiers in plant science 9, 1406, 2018 | 101 | 2018 |
| Prediction of egg storage time and yolk index based on electronic nose combined with chemometric methods J Li, S Zhu, S Jiang, J Wang LWT-Food Science and Technology 82, 369-376, 2017 | 59 | 2017 |
| Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery J Li, AN Veeranampalayam-Sivakumar, M Bhatta, ND Garst, H Stoll, ... Plant Methods 15, 1-13, 2019 | 41 | 2019 |
| Automatic wheat lodging detection and mapping in aerial imagery to support high-throughput phenotyping and in-season crop management B Zhao, J Li, PS Baenziger, V Belamkar, Y Ge, J Zhang, Y Shi Agronomy 10 (11), 1762, 2020 | 25 | 2020 |
| Evaluation of UAV-derived multimodal remote sensing data for biomass prediction and drought tolerance assessment in bioenergy sorghum J Li, DP Schachtman, CF Creech, L Wang, Y Ge, Y Shi The Crop Journal 10 (5), 1363-1375, 2022 | 23 | 2022 |
| Improved chlorophyll and water content estimations at leaf level with a hybrid radiative transfer and machine learning model J Li, NK Wijewardane, Y Ge, Y Shi Computers and Electronics in Agriculture 206, 107669, 2023 | 20 | 2023 |
| Improving model robustness for soybean iron deficiency chlorosis rating by unsupervised pre-training on unmanned aircraft system derived images J Li, C Oswald, GL Graef, Y Shi Computers and Electronics in Agriculture 175, 105557, 2020 | 12 | 2020 |
| Positioning accuracy assessment of a commercial RTK UAS B Zhao, J Li, L Wang, Y Shi Autonomous Air and Ground Sensing Systems for Agricultural Optimization and …, 2020 | 10 | 2020 |
| Investigating the potential of satellite imagery for high-throughput field phenotyping applications S Sankaran, C Zhang, JP Hurst, A Marzougui, ... Autonomous air and ground sensing systems for agricultural optimization and …, 2020 | 9 | 2020 |
| Toward accurate estimating of crop leaf stomatal conductance combining thermal IR imaging, weather variables, and machine learning L Zhao, L Wang, J Li, G Bai, Y Shi, Y Ge Autonomous air and ground sensing systems for agricultural optimization and …, 2021 | 8 | 2021 |
| Investigate the potential of UAS-based thermal infrared imagery for maize leaf area index estimation L Wang, J Li, L Zhao, B Zhao, G Bai, Y Ge, Y Shi Autonomous Air and Ground Sensing Systems for Agricultural Optimization and …, 2021 | 6 | 2021 |
| Integrating UAV hyperspectral data and radiative transfer model simulation to quantitatively estimate maize leaf and canopy nitrogen content J Li, Y Ge, LA Puntel, DM Heeren, G Bai, GR Balboa, JA Gamon, ... International Journal of Applied Earth Observation and Geoinformation 129 …, 2024 | 2 | 2024 |
| Principal variable selection to explain grain yield variation in winter wheat from UAV-derived phenotypic traits J Li, M Bhatta, ND Garst, H Stoll, AN Veeranampalayam-Sivakumar, ... 2019 ASABE Annual International Meeting, 1, 2019 | 1 | 2019 |
| Combining machine learning with a mechanistic model to estimate maize nitrogen content from UAV-acquired hyperspectral imagery J Li, Y Ge, L Puntel, D Heeren, G Balboa, Y Shi Autonomous Air and Ground Sensing Systems for Agricultural Optimization and …, 2023 | | 2023 |
| Uumanned Aerial Vehicle Data Analysis For High-throughput Plant Phenotyping J Li | | 2019 |
| Breeding for Increased Water Use Efficiency in Corn (Maize) Using a Low-altitude Unmanned Aircraft System Y Shi, AN Veeranampalayam-Sivakumar, J Li, Y Ge, JC Schnable, ... AGU Fall Meeting Abstracts 2017, B51A-1774, 2017 | | 2017 |
Yeyin ShiAssociate Professor and Agricultural Intelligence Engineer at University of Nebraska-Lincoln在 unl.edu 的电子邮件经过验证
