Abstract

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.

Due to having to go online, we are restricting the talks to University of Cambridge and alumni to keep them as informal as possible.

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