Department of Agronomy, Iowa State University, Ames, IA, USA.
Department of Mechanical Engineering, Iowa State University, Ames, IA, USA.
Trends Plant Sci. 2018 Oct;23(10):883-898. doi: 10.1016/j.tplants.2018.07.004. Epub 2018 Aug 10.
Deep learning (DL), a subset of machine learning approaches, has emerged as a versatile tool to assimilate large amounts of heterogeneous data and provide reliable predictions of complex and uncertain phenomena. These tools are increasingly being used by the plant science community to make sense of the large datasets now regularly collected via high-throughput phenotyping and genotyping. We review recent work where DL principles have been utilized for digital image-based plant stress phenotyping. We provide a comparative assessment of DL tools against other existing techniques, with respect to decision accuracy, data size requirement, and applicability in various scenarios. Finally, we outline several avenues of research leveraging current and future DL tools in plant science.
深度学习（DL）是机器学习方法的一个分支，已经成为一种通用工具，可以整合大量异构数据，并对复杂和不确定的现象进行可靠的预测。这些工具越来越多地被植物科学界用来处理现在通过高通量表型和基因型经常收集的大型数据集。我们回顾了最近利用 DL 原理进行基于数字图像的植物胁迫表型分析的工作。我们比较了 DL 工具与其他现有技术在决策准确性、数据大小要求以及在各种场景中的适用性方面的差异。最后，我们概述了利用当前和未来的 DL 工具在植物科学中开展研究的几个方向。
