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

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

Abstract

© 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.

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(1), Article 2. https://doi.org/10.1007/s00138-019-01051-7

• 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
• DOI: https://doi.org/10.1007/s00138-019-01051-7
• Keywords: Pixel-wise segmentation for plant phenotyping; Lightweight deep convolutional neural networks; Separable convolutions; Singular value decomposition
• Public URL: https://hull-repository.worktribe.com/output/3136038
• Additional Information: Received: 2 October 2018; Revised: 11 April 2019; Accepted: 6 November 2019; First Online: 17 December 2019