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Sharing the Right Data Right: A Symbiosis with Machine Learning.
Sotirios A Tsaftaris1, Hanno Scharr2
1Institute for Digital Communications, School of Engineering, University of Edinburgh, Edinburgh EH9 3FG, UK.
Trends in Plant Science
|December 1, 2018
Summary
The first open-access plant phenotyping dataset was created to address a lack of data for machine learning (ML). This dataset improved ML performance in plant image analysis.
Area of Science:
- Plant biology
- Computer science
- Data science
Background:
- Machine learning (ML) advancements were not being utilized in plant phenotyping due to insufficient data.
- Image-based plant phenotyping requires large, accessible datasets for ML model training.
Purpose of the Study:
- To introduce the first open-access dataset specifically designed for machine learning in image-based plant phenotyping.
- To facilitate interdisciplinary collaboration and accelerate progress in plant phenotyping research.
Main Methods:
- Development and release of an open-access image dataset for plant phenotyping.
- Utilizing the dataset to train and evaluate machine learning models for phenotyping tasks.
Main Results:
- The dataset enabled significant performance improvements in key plant phenotyping tasks.
- Increased awareness and fostered interdisciplinary research between plant science and ML communities.
Conclusions:
- The availability of open-access, ML-ready datasets is crucial for advancing plant phenotyping.
- This initiative demonstrated the potential of machine learning to revolutionize plant science research.
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