Crop Science Seminar: Combining multi-scale phenotyping, AI-powered analysis with genetic mapping studies to connect lab-based plant research with in-field crop improvement

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• Ji Zhou, Head of Data Science, NIAB
• Wednesday 06 April 2022, 10:30-11:30
• Online.

If you have a question about this talk, please contact Katherine Maltby.

Recent advances of imaging and sensing technologies, machine learning, computer vision and big-data analytics open new opportunities for plant research. Complex traits such as flowering, canopy structure and yield formation can be dynamically characterised, helping plant researchers understand growth patterns of key agronomic traits under varied environmental conditions. These methodological advances can also enable biological discoveries to unravel the genetics behind target traits with quantifiable evidence at the cell, organ, tissue, plant, and population levels. In this talk, the speaker will start with NIAB ’s seed l phenotyping lab (i.e. SeedGerm 2.0 and hyperspectral seed imaging with Videometer), then link lab-based discoveries to plant early establishment in the field through aerial phenotyping and the AirMeasurer platform. He will demonstrate how to measure dynamic traits, their applications in modelling plant growth and development, as well as their usefulness in genetic mapping studies. Also, the speaker will briefly introduce phenomics research carried out by his China-UK lab, including aerial phenotyping of orchard fruits, 3D trait analysis for screening resource use efficiency wheat varieties, embedded AI techniques for yield prediction in wheat, and spikelet-related trait analysis for rice, demonstrating the great potential of plant phenomics in addressing challenging biological questions.

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This talk is part of the Plant Sciences Departmental Seminars series.

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