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Towards infield, live plant phenotyping using a reduced-parameter CNN

Atanbori, John; French, Andrew P.; Pridmore, Tony P.

Authors

John Atanbori

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ANDREW FRENCH andrew.p.french@nottingham.ac.uk
Professor of Computer Science

TONY PRIDMORE tony.pridmore@nottingham.ac.uk
Professor of Computer Science



Abstract

There is an increase in consumption of agricultural produce as a result of the rapidly growing human population, particularly in developing nations. This has triggered high-quality plant phenotyping re- search to help with the breeding of high yielding plants that can adapt to our continuously changing climate. Novel, low-cost, fully automated plant phenotyping systems, capable of in-field deployment are required to help identify quantitative plant phenotypes. The identification of quantitative plant phenotypes is a key challenge which relies heavily on the precise segmentation of plant images. Recently, the plant phenotyping community has started to use very deep Convolutional Neural Networks (CNNs) to help tackle this fundamental problem. How- ever, these very deep CNNs rely on some millions of model parameters and generate very large weight matrices, thus making them difficult to deploy in-field on low-cost, resource-limited devices.

Citation

Atanbori, J., French, A. P., & Pridmore, T. P. (2020). Towards infield, live plant phenotyping using a reduced-parameter CNN. Machine Vision and Applications, 31, Article 2. https://doi.org/10.1007/s00138-019-01051-7

Journal Article Type Article
Acceptance Date Nov 6, 2019
Online Publication Date Dec 17, 2019
Publication Date 2020-01
Deposit Date Sep 13, 2019
Publicly Available Date Mar 29, 2024
Journal Machine Vision and Applications
Print ISSN 0932-8092
Electronic ISSN 1432-1769
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 31
Article Number 2
DOI https://doi.org/10.1007/s00138-019-01051-7
Keywords Hardware and Architecture; Software; Computer Vision and Pattern Recognition; Computer Science Applications
Public URL https://nottingham-repository.worktribe.com/output/2611579
Publisher URL https://link.springer.com/article/10.1007/s00138-019-01051-7
Additional Information Received: 2 October 2018; Revised: 11 April 2019; Accepted: 6 November 2019; First Online: 17 December 2019

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