Yiting Xie
Higher Degree by Research Candidate
School of Agriculture, Food and Wine
Faculty of Sciences, Engineering and Technology
After completing his Master of Science at the University of Adelaide, Yiting pursued a PhD at the same institution. He conducted research at the Plant Accelerator, the Adelaide branch of the Australian Plant Phenomics Facility, and the ARC ITTC for Future Crops Research at the University of Adelaide. Yiting's research interests lie in machine vision and machine learning, particularly focusing on deep learning applications for imaging-based plant phenotyping within the context of precision agriculture.Yiting's ongoing research project focuses on developing an efficient, accurate, and cost-effective image-based crop anthesis prediction method for wheat and canola in genetically modified field trials. This method aims to reduce field flowering inspection costs associated with genetically modified field trials while streamlining regulatory compliance processes. In addition to his academic pursuits, Yiting is passionate about plant photography, taking pleasure in capturing the intricate details and beauty of plants from various angles.
Yiting's research project is dedicated to developing an efficient, accurate, and cost-effective image-based method for predicting crop anthesis in wheat and canola during genetically modified field trials. This innovative approach aims to decrease the costs associated with field flowering inspections in such trials, concurrently facilitating the streamlining of regulatory compliance processes.
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Language Competencies
Language Competency Chinese (Cantonese) Can read, write, speak, understand spoken and peer review Chinese (Mandarin) Can read, write, speak, understand spoken and peer review English Can read, write, speak, understand spoken and peer review -
Education
Date Institution name Country Title 2019 - 2021 The University of Adelaide Australia Master of Science (Plant Breeding Innovation) 2016 - 2019 The University of Adelaide Australia Bachelor of Agricultural Sciences -
Research Interests
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Journals
Year Citation 2025 Xie, Y., Roy, S. J., Schilling, R. K., & Liu, H. (2025). Multi-modal few-shot learning for anthesis prediction of individual wheat plants. Plant Phenomics, 7(3), 100091-1-100091-18.
2024 Xie, Y., Plett, D., Evans, M., Garrard, T., Butt, M., Clarke, K., & Liu, H. (2024). Hyperspectral imaging detects biological stress of wheat for early diagnosis of crown rot disease. Computers and Electronics in Agriculture, 217, 108571.
Scopus28 WoS242022 Xie, Y., Plett, D., & Liu, H. (2022). Detecting Crown Rot Disease in Wheat in Controlled Environment Conditions Using Digital Color Imaging and Machine Learning. AgriEngineering, 4(1), 141-155.
Scopus12 WoS102021 Xie, Y., Plett, D., & Liu, H. (2021). The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat. AgriEngineering, 3(4), 924-941.
Scopus17 WoS16
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