The University of Queensland

Quantification of the three-dimensional root system architecture using an automated rotating imaging system

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

Unsupervised plot-scale LAI phenotyping via UAV-based imaging, modelling, and machine learning

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

Phenological optimization of late reproductive phase for raising wheat yield potential in irrigated mega-environments

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

Using a gene-based phenology model to identify optimal flowering periods of spring wheat in irrigated mega-environments

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

Comparison of modelling strategies to estimate phenotypic values from an unmanned aerial vehicle with spectral and temporal vegetation indexes

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

Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops

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

A field-based high-throughput method for acquiring canopy architecture using unmanned aerial vehicle images

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

Pixel size of aerial imagery constrains the applications of unmanned aerial vehicle in crop breeding

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

Image-based dynamic quantification and high-accuracy 3D evaluation of canopy structure of plant populations

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

Estimation of plant height using a high throughput phenotyping platform based on unmanned aerial vehicle and self-calibration: Example for sorghum breeding

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

基于多视角立体视觉的植株三维重建与精度评估

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

Integration of data across scales to predict genotype performance in National Variety Trials

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.