Predicting Plant Growth from Time-Series Data Using Deep Learning
Phenotyping involves the quantitative assessment of the anatomical, biochemical, and physiological plant traits. Natural plant growth cycles can be extremely slow, hindering the experimental processes of phenotyping. Deep learning offers a great deal of support for automating and addressing key plan...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/3/331 |
| _version_ | 1797409501571186688 |
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| author | Robail Yasrab Jincheng Zhang Polina Smyth Michael P. Pound |
| author_facet | Robail Yasrab Jincheng Zhang Polina Smyth Michael P. Pound |
| author_sort | Robail Yasrab |
| collection | DOAJ |
| description | Phenotyping involves the quantitative assessment of the anatomical, biochemical, and physiological plant traits. Natural plant growth cycles can be extremely slow, hindering the experimental processes of phenotyping. Deep learning offers a great deal of support for automating and addressing key plant phenotyping research issues. Machine learning-based high-throughput phenotyping is a potential solution to the phenotyping bottleneck, promising to accelerate the experimental cycles within phenomic research. This research presents a study of deep networks’ potential to predict plants’ expected growth, by generating segmentation masks of root and shoot systems into the future. We adapt an existing generative adversarial predictive network into this new domain. The results show an efficient plant leaf and root segmentation network that provides predictive segmentation of what a leaf and root system will look like at a future time, based on time-series data of plant growth. We present benchmark results on two public datasets of <i>Arabidopsis</i> (<i>A. thaliana</i>) and <i>Brassica rapa</i> (Komatsuna) plants. The experimental results show strong performance, and the capability of proposed methods to match expert annotation. The proposed method is highly adaptable, trainable (transfer learning/domain adaptation) on different plant species and mutations. |
| first_indexed | 2024-03-09T04:15:26Z |
| format | Article |
| id | doaj.art-d6025be440d741cc8403852254d053a9 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T04:15:26Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d6025be440d741cc8403852254d053a92023-12-03T13:55:30ZengMDPI AGRemote Sensing2072-42922021-01-0113333110.3390/rs13030331Predicting Plant Growth from Time-Series Data Using Deep LearningRobail Yasrab0Jincheng Zhang1Polina Smyth2Michael P. Pound3Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UKComputer Vision Laboratory, School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UKComputer Vision Laboratory, School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UKComputer Vision Laboratory, School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UKPhenotyping involves the quantitative assessment of the anatomical, biochemical, and physiological plant traits. Natural plant growth cycles can be extremely slow, hindering the experimental processes of phenotyping. Deep learning offers a great deal of support for automating and addressing key plant phenotyping research issues. Machine learning-based high-throughput phenotyping is a potential solution to the phenotyping bottleneck, promising to accelerate the experimental cycles within phenomic research. This research presents a study of deep networks’ potential to predict plants’ expected growth, by generating segmentation masks of root and shoot systems into the future. We adapt an existing generative adversarial predictive network into this new domain. The results show an efficient plant leaf and root segmentation network that provides predictive segmentation of what a leaf and root system will look like at a future time, based on time-series data of plant growth. We present benchmark results on two public datasets of <i>Arabidopsis</i> (<i>A. thaliana</i>) and <i>Brassica rapa</i> (Komatsuna) plants. The experimental results show strong performance, and the capability of proposed methods to match expert annotation. The proposed method is highly adaptable, trainable (transfer learning/domain adaptation) on different plant species and mutations.https://www.mdpi.com/2072-4292/13/3/331imagingmachine learningcrop phenotypingplant phenotypingimaging sensorsimagery algorithms |
| spellingShingle | Robail Yasrab Jincheng Zhang Polina Smyth Michael P. Pound Predicting Plant Growth from Time-Series Data Using Deep Learning Remote Sensing imaging machine learning crop phenotyping plant phenotyping imaging sensors imagery algorithms |
| title | Predicting Plant Growth from Time-Series Data Using Deep Learning |
| title_full | Predicting Plant Growth from Time-Series Data Using Deep Learning |
| title_fullStr | Predicting Plant Growth from Time-Series Data Using Deep Learning |
| title_full_unstemmed | Predicting Plant Growth from Time-Series Data Using Deep Learning |
| title_short | Predicting Plant Growth from Time-Series Data Using Deep Learning |
| title_sort | predicting plant growth from time series data using deep learning |
| topic | imaging machine learning crop phenotyping plant phenotyping imaging sensors imagery algorithms |
| url | https://www.mdpi.com/2072-4292/13/3/331 |
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