Dr Pengcheng Hu
Postdoctoral Research Fellow
School of Agriculture and Food Sciences
Researcher biography
Dr Pengcheng Hu received his PhD degree in agriculture science at China Agricultural University (CAU). He conducted most part of her PhD research in CSIRO under the CSIRO-Chinese Ministry of Education joint PhD program with a scholarship from China Scholarship Council. His research focuses on the crop modelling, high throughput phenotyping, and crop genotype to phenotype prediction and climate adaptation.
Journal Articles
Wu, Qian, Wu, Jie, Hu, Pengcheng, Zhang, Weixin, Ma, Yuntao, Yu, Kun, Guo, Yan, Cao, Jing, Li, Huayong, Li, Baiming, Yao, Yuyang, Cao, Hongxin and Zhang, Wenyu (2023). Quantification of the three-dimensional root system architecture using an automated rotating imaging system. Plant Methods, 19 (1) 11. doi: 10.1186/s13007-023-00988-1
Chen, Qiaomin, Zheng, Bangyou, Chenu, Karine, Hu, Pengcheng and Chapman, Scott C. (2022). Unsupervised plot-scale LAI phenotyping via UAV-based imaging, modelling, and machine learning. Plant Phenomics, 2022 9768253, 1-19. doi: 10.34133/2022/9768253
Hu, Pengcheng, Chapman, Scott C., Sukumaran, Sivakumar, Reynolds, Matthew and Zheng, Bangyou (2022). Phenological optimization of late reproductive phase for raising wheat yield potential in irrigated mega-environments. Journal of Experimental Botany, 73 (12), 4236-4249. doi: 10.1093/jxb/erac144
Hu, Pengcheng, Chapman, Scott C., Dreisigacker, Susanne, Sukumaran, Sivakumar, Reynolds, Matthew and Zheng, Bangyou (2021). Using a gene-based phenology model to identify optimal flowering periods of spring wheat in irrigated mega-environments. Journal of Experimental Botany, 72 (20), 7203-7218. doi: 10.1093/jxb/erab326
Hu, Pengcheng, Chapman, Scott C., Jin, Huidong, Guo, Yan and Zheng, Bangyou (2021). Comparison of modelling strategies to estimate phenotypic values from an unmanned aerial vehicle with spectral and temporal vegetation indexes. Remote Sensing, 13 (14) 2827, 1-19. doi: 10.3390/rs13142827
Hu, Pengcheng, Chapman, Scott C. and Zheng, Bangyou (2021). Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops. Functional Plant Biology, 48 (8), 766-779. doi: 10.1071/FP20309
Liu, Fusang, Hu, Pengcheng, Zheng, Bangyou, Duan, Tao, Zhu, Binglin and Guo, Yan (2021). A field-based high-throughput method for acquiring canopy architecture using unmanned aerial vehicle images. Agricultural and Forest Meteorology, 296 108231. doi: 10.1016/j.agrformet.2020.108231
Hu, Pengcheng, Guo, Wei, Chapman, Scott C., Guo, Yan and Zheng, Bangyou (2019). Pixel size of aerial imagery constrains the applications of unmanned aerial vehicle in crop breeding. ISPRS Journal of Photogrammetry and Remote Sensing, 154, 1-9. doi: 10.1016/j.isprsjprs.2019.05.008
Hui, Fang, Zhu, Jinyu, Hu, Pengcheng, Meng, Lei, Zhu, Binglin, Guo, Yan, Li, Baoguo and Ma, Yuntao (2018). Image-based dynamic quantification and high-accuracy 3D evaluation of canopy structure of plant populations. Annals of Botany, 121 (5), 1079-1088. doi: 10.1093/aob/mcy016
Hu, Pengcheng, Chapman, Scott C., Wang, Xuemin, Potgieter, Andries, Duan, Tao, Jordan, David, Guo, Yan and Zheng, Bangyou (2018). Estimation of plant height using a high throughput phenotyping platform based on unmanned aerial vehicle and self-calibration: Example for sorghum breeding. European Journal of Agronomy, 95, 24-32. doi: 10.1016/j.eja.2018.02.004
Hu, Pengcheng, Guo, Yan, Li, Baoguo, Zhu, Jinyu and Ma, Yuntao (2015). 基于多视角立体视觉的植株三维重建与精度评估. Nongye Gongcheng Xuebao, 31 (11), 209-214. doi: 10.11975/j.issn.1002-6819.2015.11.030
Conference Paper
Chapman, Scott, Noviati, Vivi, Hu, Pengcheng, McLaren, Connar, Smith, Daniel, Choudhury, Malini, Chen, Zhi, Grunfeld, Swaantje, Zheng, Bangyou, van Eeuwijk, Fred, Bustos-Korts, Daniela, Boer, Martin, Hemerik, Jesse and Ramakers, Jip (2022). Integration of data across scales to predict genotype performance in National Variety Trials. Australasian Plant Breeding Conference, Gold Coast, QLD Australia, 9-11 May 2022.