T. Pridmore: Novel Convolutional Neural Net Architectures for Plant Image Analysis and Phenotyping

Bio Information

Tony Pridmore is Professor of Computer Science at the University of Nottingham. His research interests centre on the development of novel image-based plant phenotyping methods and, increasingly, the creation and operation of large-scale phenotyping infrastructures. Pridmore leads Nottingham’s Computer Vision Laboratory and serves on the Management Board of the Hounsfield Facility, a unique installation providing automated extraction of 3D structural descriptions of plants from X-ray data.

He is a member of the Imaging and Image Analysis Working Group of the International Plant Phenotyping Network and the International Scientific Advisory Committee of the University of Saskatchewan’s Plant Phenotyping and Imaging Research Centre (P2IRC). Tony Pridmore is Associate Editor of Plant Methods and Plant Phenomics and Director of the UKRI Technology Touching Life Network PhenomUK.

Presentation Abstract

Deep machine learning has revolutionised computer vision in recent years, achieving performance levels across a variety of tasks that surpass previous approaches and often exceed human performance. Convolutional Neural Nets (CNNs) of various forms are of particular interest, and many applications of existing architectures in the life and biological sciences have been reported.

Images of plants, however, raise particular challenges that differ from those faced in the wider computer vision community. Objects of interest are often very small but embedded in very large images, with the contextual information needed to correctly interpret those images distributed across them. Deployment of CNNs can also be problematic for a community focussed on analysing plants in their natural habitat – fields.

This talk will describe novel convolutional neural net architectures developed for shoot and root image analysis at the Computer Vision Laboratory, University of Nottingham, and the infrastructures and methods used to support their development and deployment.

Authors: Tony Pridmore, Andrew French, Michael Pound


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