Ms Yiyi Xiong


NameMs Yiyi Xiong
Email Addressyiyi.xiong@unisq.edu.au
Job TitleResearch Fellow (Crop Sensing)
QualificationsMAgSc Qld
DepartmentCentre for Agricultural Engineering (Research)
ORCIDhttps://orcid.org/0000-0001-9238-6200
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Biography

Yiyi Xiong is a skilled professional with a solid background in crop production, pest and disease management, and remote sensing analysis.

She has developed specialised proficiency in remote sensing technologies, utilising advanced UAV (drone) technology and geospatial analysis tools to monitor crop health, assess soil conditions and enhance precision agriculture practices. Her expertise supports data-driven decision-making, improving agricultural productivity and sustainability. She has worked closely with key funding bodies, including QDPI and John Deere, and has collaborated with pathologists across multiple disciplines to drive impactful agricultural solutions.

With a well-rounded academic foundation and hands-on experience, she has developed capabilities in these specialised areas. Her commitment to agricultural research and technological innovation is reflected in her ability to integrate cutting-edge solutions that address challenges in modern farming and agronomic management.

Expertise

Python, APSIM, PIX4D, Agisoft, ArcGIS, QGIS, ENVI, Blender, Machine Learning, Deep Learning

Professional Membership

Professional MembershipYear
Remote Pilot Licence
ChemCert (Chemical Accreditation)
MAgSc
Qld
2018

A review on common root rot of wheat and barley in Australia

Xiong, Yiyi, McCarthy, Cheryl, Humpal, Jacob and Percy, Cassandra. 2023. "A review on common root rot of wheat and barley in Australia." Plant Pathology. 72 (8), pp. 1347-1364. https://doi.org/10.1111/ppa.13777

Near-infrared spectroscopy and deep neural networks for early common root rot detection in wheat from multi-season trials

Xiong, Yiyi, McCarthy, Cheryl, Humpal, Jacob and Percy, Cassandra. 2024. "Near-infrared spectroscopy and deep neural networks for early common root rot detection in wheat from multi-season trials ." Agronomy Journal. 116 (5), pp. 2370-2390. https://doi.org/10.1002/agj2.21648

Non-visual common root rot disease detection using NIR spectrum and machine learning methods

Xiong, Yiyi, McCarthy, Cheryl, Humpal, Jacob and Percy, Cassandra. "Non-visual common root rot disease detection using NIR spectrum and machine learning methods." 2023 11th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). Wuhan, China 25 - 28 Jul 2023 IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/https://ieeexplore.ieee.org/xpl/conhome/10233256/proceeding