Towards low-cost image-based plant phenotyping using reduced-parameter CNN
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...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2018
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| Online Access: | https://eprints.nottingham.ac.uk/54696/ |
| _version_ | 1848799066740228096 |
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| author | Atanbori, John Chen, Feng French, Andrew P. Pridmore, Tony P. |
| author_facet | Atanbori, John Chen, Feng French, Andrew P. Pridmore, Tony P. |
| author_sort | Atanbori, John |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:29:46Z |
| format | Conference or Workshop Item |
| id | nottingham-54696 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:29:46Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-546962018-09-13T07:46:27Z https://eprints.nottingham.ac.uk/54696/ Towards low-cost image-based plant phenotyping using reduced-parameter CNN Atanbori, John Chen, Feng French, Andrew P. Pridmore, Tony P. 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. 2018-09-06 Conference or Workshop Item PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/54696/1/CNN%200023.pdf Atanbori, John, Chen, Feng, French, Andrew P. and Pridmore, Tony P. (2018) Towards low-cost image-based plant phenotyping using reduced-parameter CNN. In: CVPPP 2018: Workshop on Computer Vision Problems in Plant Phenotyping, 6 Sept 2018, Newcastle upon Tyne, UK. |
| spellingShingle | Atanbori, John Chen, Feng French, Andrew P. Pridmore, Tony P. Towards low-cost image-based plant phenotyping using reduced-parameter CNN |
| title | Towards low-cost image-based plant phenotyping using reduced-parameter CNN |
| title_full | Towards low-cost image-based plant phenotyping using reduced-parameter CNN |
| title_fullStr | Towards low-cost image-based plant phenotyping using reduced-parameter CNN |
| title_full_unstemmed | Towards low-cost image-based plant phenotyping using reduced-parameter CNN |
| title_short | Towards low-cost image-based plant phenotyping using reduced-parameter CNN |
| title_sort | towards low-cost image-based plant phenotyping using reduced-parameter cnn |
| url | https://eprints.nottingham.ac.uk/54696/ |