Pheno-Deep Counter: a unified and versatile deep learning architecture for leaf counting.

Autor: Giuffrida MV; Institute for Digital Communications, School of Engineering, University of Edinburgh, Thomas Bayes Road, EH9 3FG, Edinburgh, UK.; IMT School for Advanced Studies, Piazza S. Francesco 19, 55100, Lucca, Italy., Doerner P; School of Biological Sciences, University of Edinburgh, Mayfield Road, Edinburgh, EH9 3JR, UK., Tsaftaris SA; Institute for Digital Communications, School of Engineering, University of Edinburgh, Thomas Bayes Road, EH9 3FG, Edinburgh, UK.; The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, UK.
Jazyk: angličtina
Zdroj: The Plant journal : for cell and molecular biology [Plant J] 2018 Nov; Vol. 96 (4), pp. 880-890. Date of Electronic Publication: 2018 Sep 11.
DOI: 10.1111/tpj.14064
Abstrakt: Direct observation of morphological plant traits is tedious and a bottleneck for high-throughput phenotyping. Hence, interest in image-based analysis is increasing, with the requirement for software that can reliably extract plant traits, such as leaf count, preferably across a variety of species and growth conditions. However, current leaf counting methods do not work across species or conditions and therefore may lack broad utility. In this paper, we present Pheno-Deep Counter, a single deep network that can predict leaf count in two-dimensional (2D) plant images of different species with a rosette-shaped appearance. We demonstrate that our architecture can count leaves from multi-modal 2D images, such as visible light, fluorescence and near-infrared. Our network design is flexible, allowing for inputs to be added or removed to accommodate new modalities. Furthermore, our architecture can be used as is without requiring dataset-specific customization of the internal structure of the network, opening its use to new scenarios. Pheno-Deep Counter is able to produce accurate predictions in many plant species and, once trained, can count leaves in a few seconds. Through our universal and open source approach to deep counting we aim to broaden utilization of machine learning-based approaches to leaf counting. Our implementation can be downloaded at https://bitbucket.org/tuttoweb/pheno-deep-counter.
(© 2018 The Authors The Plant Journal published by John Wiley & Sons Ltd and Society for Experimental Biology.)
Databáze: MEDLINE
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