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

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


Andrew P. French

Tony P. Pridmore


© 2019, The Author(s). 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 research 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 infield 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. However, these very deep CNNs rely on some millions of model parameters and generate very large weight matrices, thus making them difficult to deploy infield on low-cost, resource-limited devices. We explore how to compress existing very deep CNNs for plant image segmentation, thus making them easily deployable infield and on mobile devices. In particular, we focus on applying these models to the pixel-wise segmentation of plants into multiple classes including background, a challenging problem in the plant phenotyping community. We combined two approaches (separable convolutions and SVD) to reduce model parameter numbers and weight matrices of these very deep CNN-based models. Using our combined method (separable convolution and SVD) reduced the weight matrix by up to 95% without affecting pixel-wise accuracy. These methods have been evaluated on two public plant datasets and one non-plant dataset to illustrate generality. We have successfully tested our models on a mobile device.


Atanbori, J., French, A. P., & Pridmore, T. P. (2020). Towards infield, live plant phenotyping using a reduced-parameter CNN. Machine Vision and Applications, 31(1),

Journal Article Type Article
Acceptance Date Sep 10, 2019
Online Publication Date Dec 17, 2019
Publication Date 2020-01
Deposit Date Nov 13, 2019
Publicly Available Date Dec 23, 2019
Journal Machine Vision and Applications
Print ISSN 0932-8092
Electronic ISSN 1432-1769
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 31
Issue 1
Article Number 2
Keywords Pixel-wise segmentation for plant phenotyping; Lightweight deep convolutional neural networks; Separable convolutions; Singular value decomposition
Public URL
Additional Information Received: 2 October 2018; Revised: 11 April 2019; Accepted: 6 November 2019; First Online: 17 December 2019


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Copyright Statement
© The Author(s) 2019. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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