Institute for Digital Communications, School of Engineering, University of Edinburgh, Edinburgh EH9 3FG, UK.
Institute of Bio- and Geosciences: Plant Sciences (IBG-2) Forschungszentrum Jülich GmbH, D-52425, Jülich, Germany.
Trends Plant Sci. 2019 Feb;24(2):99-102. doi: 10.1016/j.tplants.2018.10.016. Epub 2018 Nov 26.
In 2014 plant phenotyping research was not benefiting from the machine learning (ML) revolution because appropriate data were lacking. We report the success of the first open-access dataset suitable for ML in image-based plant phenotyping suitable for machine learning, fuelling a true interdisciplinary symbiosis, increased awareness, and steep performance improvements on key phenotyping tasks.
在 2014 年，植物表型研究并没有受益于机器学习 (ML) 革命，因为缺乏合适的数据。我们报告了第一个适用于基于图像的植物表型学的机器学习的开放获取数据集的成功，它促进了真正的跨学科共生，提高了人们的认识，并在关键表型任务上取得了显著的性能提升。
