Huajian Liu

Research Interests

Agricultural Biotechnology Diagnostics Artificial Intelligence & Image Processing Computer Vision

Mr Huajian Liu

Grant-Funded Research Fellow

School of Agriculture, Food and Wine

College of Science

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

In 2018, I finished my PhD study of machine vision for invertebrate detection on crops at the University of South Australia. In the same year, I joined The Plant Accelerator, the Adelaide node of the Australian Plant Phenomic Facility, located at the Waite campus of The University of Adelaide, as a post-doctoral researcher. Since then, I have been specialising in machine vision and machine learning for plant phenotyping and precision agriculture, especially for hyperspectral imaging-based plant phenotyping. I am currently a grant-funded researcher and my research interests include plant nutrient estimation, plant disease detection, drought and salt stress tolerance, plant growing status estimation, invertebrate pest detection, machine learning and deep learning, optical sensing system design, bio-inspired machine vision system, bird vision, insect vision, high-dimensional colour space,  3D model reconstruction and object recognition.  

Date Position Institution name
2018 - ongoing Postdoctoral Fellow/Grant-funded Researcher University of Adelaide

Date Institution name Country Title
2014 - 2018 University of South Australia Australia PhD

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.
DOI
2025 Xie, Y., Roy, S. J., Schilling, R. K., Berger, B., & Liu, H. (2025). Hyperspectral-based classification of individual wheat plants into fine-scale reproductive stages. Plant Methods, 21(1), 146-1-146-20.
DOI
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.
DOI Scopus33 WoS30
2024 Liu, H., Perry, K. D., Lu, T. F., Wu, T., & Buhl, C. (2024). Machine vision solutions for monitoring pest snails in australian no-till cropping fields: An exploration of spectral characteristics and detectability. Journal of Agriculture and Food Research, 16, 12 pages.
DOI Scopus2 WoS1
2024 Hume, R., Marschner, P., Mason, S., Schilling, R. K., Liu, H., Jewell, N., . . . Mosley, L. M. (2024). Assessing wheat responses to soil acidification and liming using hyperspectral imaging. Plant and Soil, 511(1), 925-943.
DOI
2023 Wu, T., Dai, J., Shen, P., Liu, H., & Wei, Y. (2023). Seedscreener: A novel integrated wheat germplasm phenotyping platform based on NIR-feature detection and 3D-reconstruction. Computers and Electronics in Agriculture, 215, 108378.
DOI Scopus4 WoS4
2023 Liu, H., & Xu, Z. (2023). Editorial: Machine vision and machine learning for plant phenotyping and precision agriculture. Frontiers in Plant Science, 14, 1331918.
DOI Scopus11 Europe PMC3
2022 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.
DOI Scopus14 WoS12
2022 Ball, K. R., Liu, H., Brien, C., Berger, B., Power, S. A., & Pendall, E. (2022). Hyperspectral imaging predicts yield and nitrogen content in grass-legume polycultures. Precision Agriculture, 23(6), 2270-2288.
DOI Scopus12 WoS11
2021 Liu, H., & Chahl, J. S. (2021). Proximal detecting invertebrate pests on crops using a deep residual convolutional neural network trained by virtual images. Artificial Intelligence in Agriculture, 5, 13-23.
DOI Scopus26 WoS19
2021 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.
DOI Scopus18 WoS17
2020 Liu, H., Bruning, B., Garnett, T., & Berger, B. (2020). Hyperspectral imaging and 3D technologies for plant phenotyping: From satellite to close-range sensing. Computers and Electronics in Agriculture, 175, 105621-1-105621-13.
DOI Scopus113 WoS102
2020 Bruning, B., Berger, B., Lewis, M., Liu, H., & Garnett, T. (2020). Approaches, applications, and future directions for hyperspectral vegetation studies: An emphasis on yield‐limiting factors in wheat. Plant Phenome Journal, 3(1), e20007-1-e20007-22.
DOI Scopus37
2020 Liu, H., Bruning, B., Garnett, T., & Berger, B. (2020). The performances of hyperspectral sensors for proximal sensing of nitrogen levels in wheat. Sensors, 20(16), 4550-1-4550-21.
DOI Scopus37 WoS34 Europe PMC16
2019 Bruning, B., Liu, H., Brien, C., Berger, B., Lewis, M., & Garnett, T. (2019). The Development of Hyperspectral Distribution Maps to Predict the Content and Distribution of Nitrogen and Water in Wheat (Triticum aestivum).. Frontiers in plant science, 10, 1380.
DOI Scopus69 WoS63 Europe PMC19
2018 Liu, H., & Chahl, J. (2018). A multispectral machine vision system for invertebrate detection on green leaves. Computers and Electronics in Agriculture, 150, 279-288.
DOI Scopus43 WoS41 Europe PMC2
2018 Liu, H., Lee, S. -H., & Chahl, J. S. (2018). Registration of multispectral 3D points for plant inspection. Precision Agriculture, 19(3), 513-536.
DOI Scopus27 WoS21
2017 Liu, H., Lee, S. -H., & Chahl, J. S. (2017). A review of recent sensing technologies to detect invertebrates on crops. Precision Agriculture, 18(4), 635-666.
DOI Scopus58 WoS48 Europe PMC6
2017 Liu, H., Lee, S. -H., & Chahl, J. S. (2017). An evaluation of the contribution of ultraviolet in fused multispectral images for invertebrate detection on green leaves. Precision Agriculture, 18(4), 667-683.
DOI Scopus10 WoS9
2017 Liu, H., Lee, S. -H., & Chahl, J. S. (2017). A multispectral 3-D vision system for invertebrate detection on crops. IEEE Sensors Journal, 17(22), 7502-7515.
DOI Scopus35 WoS25
2017 Liu, H., Lee, S. -H., & Chahl, J. S. (2017). Transformation of a high-dimensional color space for material classification. Journal of the Optical Society of America A, 34(4), 523.
DOI Scopus20 WoS20 Europe PMC8
2014 Liu, H., Lee, S. H., & Saunders, C. (2014). Development of a machine vision system for weed detection during both of off-season and in-season in broadacre no-tillage cropping lands. American Journal of Agricultural and Biological Sciences, 9(2), 174-193.
DOI Scopus24
2013 Liu, H., Saunders, C., & Lee, S. H. (2013). Development of a proximal machine vision system for off-season weed mapping in broadacre no-tillage fallows. Journal of computer science, 9(12), 1803-1821.
DOI Scopus12
- Liu, H. (n.d.). Machine vision solutions for monitoring pest snails in Australian no-till cropping fields: An exploration of spectral characteristics and detectability.
DOI

Year Citation
2019 Liu, H., Bruning, B., Berger, B., & Garnett, T. (2019). Green plant segmentation in hyperspectral images using SVM and hyper-hue. Poster session presented at the meeting of Proceedings: 7th International Workshop on Image Analysis Methods for the Plant Sciences (IAMPS 2019). Lyon, France.
2018 Liu, H. (2018). Invertebrate pest detection on crops using multispectral images and 3D vision. Poster session presented at the meeting of The 5th International Plant Phenotyping Symposium. Adelaide.

Year Citation
2018 Liu, H., Lee, S. -H., & Chahl, J. (2018). MACHINE VISION FOR DETECTION OF INVERTEBRATES ON CROPS.
2015 Liu, H., Saunder, C., & Lee, S. -H. (2015). Development of the Algorithms and Mechanism of a Machine Vision System for Both Summer and In-season Weed Mapping in Broadacre No-till Cropping Lands.

Year Citation
- Liu, H., Berger, B., Garnett, T., & Bruning, B. (n.d.). public data for wheat_n experiment.
DOI
- Liu, H. (n.d.). The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat.
DOI
- Liu, H. (n.d.). Detecting Crown Rot Disease in Wheat in Controlled Environment Conditions Using Digital Color Imaging and Machine Learning.
DOI
- Liu, H., Ball, K., & Brien, C. (n.d.). Hyperspectral imaging predicts yield and nitrogen content in grass-legume polyculturesem.
DOI
- Liu, H. (n.d.). APPF TPA phenotyping dataset: UA TPA (Liu) - Wheat.
DOI

 

Dates Roles Grants Amount
2023-2026 CI GRDC project (PROC-9176789 ) In-field high-throughput phenotyping for barley net blotch $830,000
2022-2024 CI GRDC project (PROC-9176394) "More effective control of pest molluscs (snails and slugs) in Australian grain crops (RFT)" $2, 831, 392
2021-2022 CI Research Roadmap "Improving detection and monitoring of biosecurity threats using drones, field robots and machine learning" $43, 330
2020-2021 Lead CI Yitpi Foundation Awards “Hyperspectral phenotyping for the rapid identification and quantification of crown rot in wheat” $18,150
2019 CI GRDC project (1977340) "New methods for snail control " $140,250

 

Date Role Research Topic Program Degree Type Student Load Student Name
2022 - 2025 Principal Supervisor Field-Based High-Throughput Phenotyping for Anthesis Prediction in
Individual Wheat and Canola Plants		 Doctor of Philosophy Doctorate Full Time Mr Yiting Xie

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