Towards low-cost image-based plant phenotyping using reduced-parameter CNN

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

Abstract

Segmentation is the core of most plant phenotyping applications. Current state-of-the-art plant phenotyping applications rely on deep Convolutional Neural Networks (CNNs). However, these networks have many layers and parameters, increasing training and test times. Phenotyping applications relying on these deep CNNs are also often difficult if not impossible to deploy on limited-resource devices. We present our work which investigates parameter reduction in deep neural networks, a first step to moving plant phenotyping applications in-field and on low-cost devices with limited resources. We re-architect four baseline deep neural networks (creating what we term "Lite CNNs") by reducing their parameters whilst making them deeper to avoid the problem of overfitting. We achieve state-of-the-art, comparable performance on our "Lite" CNNs versus the baselines. We also introduce a simple global hyper-parameter (alpha) that provides an efficient trade-off between parameter-size and accuracy.

Citation

Atanbori, J., Chen, F., French, A. P., & Pridmore, T. (2018, September). Towards low-cost image-based plant phenotyping using reduced-parameter CNN. Presented at CVPPP 2018: Workshop on Computer Vision Problems in Plant Phenotyping

• Conference Name: CVPPP 2018: Workshop on Computer Vision Problems in Plant Phenotyping
• Start Date: Sep 6, 2018
• End Date: Sep 6, 2018
• Acceptance Date: Jul 18, 2018
• Publication Date: Sep 6, 2018
• Deposit Date: Aug 16, 2018
• Publicly Available Date: Aug 16, 2018
• Public URL: https://nottingham-repository.worktribe.com/output/1036479
• Related Public URLs: https://www.plant-phenotyping.org/CVPPP2018; http://bmvc2018.org/
• Additional Information: Workshop is held at 29th British Machine Vision Conference, Northumbria University, UK, 4-6 September 2018.
• Contract Date: Aug 16, 2018