| Field-based high-throughput phenotyping of plant height in sorghum using different sensing technologies X Wang, D Singh, S Marla, G Morris, J Poland Plant Methods 14 (54), 2018 | 134 | 2018 |
| High-Throughput Phenotyping Enabled Genetic Dissection of Crop Lodging in Wheat D Singh, X Wang, U Kumar, L Gao, M Noor, M Imtiaz, RP Singh, J Poland Frontiers in Plant Science 10, 394, 2019 | 130 | 2019 |
| Development of a field-based high-throughput mobile phenotyping platform JP Jared Barker III, Naiqian Zhang, Joshua Sharon, Ryan Steeves, Xu Wang ... Computers and Electronics in Agriculture 122, 74-85, 2016 | 127 | 2016 |
| Global wheat head detection 2021: An improved dataset for benchmarking wheat head detection methods E David, M Serouart, D Smith, S Madec, K Velumani, S Liu, X Wang, ... Plant Phenomics, 2021 | 84 | 2021 |
| High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat X Wang, H Xuan, B Evers, S Shrestha, R Pless, J Poland GigaScience 8 (11), 2019 | 76 | 2019 |
| The Aegilops ventricosa 2NvS segment in bread wheat: cytology, genomics and breeding L Gao, DH Koo, P Juliana, T Rife, D Singh, C Lemes da Silva, T Lux, ... Theoretical and Applied Genetics 134, 529-542, 2021 | 54 | 2021 |
| Efficient crop model parameter estimation and site characterization using large breeding trial data sets A Lamsal, SM Welch, JW Jones, KJ Boote, A Asebedo, J Crain, X Wang, ... Agricultural systems 157, 170-184, 2017 | 23 | 2017 |
| Approaches for geospatial processing of field-based high-throughput plant phenomics data from ground vehicle platforms X Wang, KR Thorp, JW White, AN French, JA Poland Transactions of the ASABE 59 (5), 1053-1067, 2016 | 22 | 2016 |
| Improved Accuracy of High-Throughput Phenotyping From Unmanned Aerial Systems by Extracting Traits Directly From Orthorectified Images X Wang, P Silva, NM Bello, D Singh, B Evers, S Mondal, FP Espinosa, ... Frontiers in Plant Science 11, 1616, 2020 | 15 | 2020 |
| Evaluation of field-based single plant phenotyping for wheat breeding J Crain, X Wang, B Evers, J Poland The Plant Phenome Journal 5 (1), 2022 | 10 | 2022 |
| Genomics and Phenomics Enabled Prebreeding Improved Early-Season Chilling Tolerance in Sorghum S Marla, T Felderhoff, C Hayes, R Perumal, X Wang, J Poland, PG Morris G3 Genes|Genomes|Genetics, 2023 | 4 | 2023 |
| Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics M Togninalli, X Wang, T Kucera, S Shrestha, P Juliana, S Mondal, F Pinto, ... Bioinformatics, 2023 | 4 | 2023 |
| Breeding program optimization for genomic selection in winter wheat M Calvert, B Evers, X Wang, A Fritz, J Poland bioRxiv, 2020.10. 07.330415, 2020 | 3 | 2020 |
| Applied phenomics and genomics for improving barley yellow dwarf resistance in winter wheat P Silva, B Evers, A Kieffaber, X Wang, R Brown, L Gao, A Fritz, J Crain, ... G3 Genes| Genomes| Genetics 12 (7), 2022 | 2 | 2022 |
| Small plot identification from video streams for high-throughput phenotyping of large breeding populations with unmanned aerial systems X Wang, C Amos, M Lucas, G Williams, J Poland Autonomous Air and Ground Sensing Systems for Agricultural Optimization and …, 2019 | 2 | 2019 |
| Artificial intelligence/machine learning-assisted near-infrared/optical biosensing for plant phenotyping X Wang, X Zhou, L Ji, K Shen Machine Learning and Artificial Intelligence in Chemical and Biological …, 2024 | | 2024 |
| An extended omnigenic model explains genome-phenome relationships for complex traits in global sorghum diversity Z Hu, X Wang, SR Marla, J Poland, GP Morris bioRxiv, 2024.04. 29.591686, 2024 | | 2024 |
| Strawberry Canopy Structural Parameters Estimation and Growth Analysis from UAV Multispectral Imagery Using a Geospatial Tool C Zheng, A Abd-Elrahman, VM Whitaker, C Dalid, X Wang Available at SSRN 4689301, 2024 | | 2024 |
| Field phenomics reveals genetic variation for transpiration response to vapor pressure deficit in sorghum R Raymundo, X Wang, T Felderhoff, S Sexton-Bowser, J Poland, ... bioRxiv, 2023.06. 23.546345, 2023 | | 2023 |
| Supporting data for: Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics X Wang, S Shrestha, P Juliana, S Mondal, J Poland, FP Espinosa, ... Dryad, 2023 | | 2023 |
Jesse PolandProfessor, King Abdullah University of Science and Technology在 kaust.edu.sa 的电子邮件经过验证
