Predicting Plant Growth from Time-Series Data Using Deep Learning
Predicting Plant Growth from Time-Series Data Using Deep Learning
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DOI：
10.3390/rs13030331
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发表时间：
2021-02-01
期刊：
影响因子：
5
通讯作者：
Pound, Michael P.
中科院分区：
文献类型：
--
作者：
Yasrab, Robail;Zhang, Jincheng;Pound, Michael P.
表型分析涉及对植物的解剖、生化和生理性状的定量评估。天然植物生长周期可能非常缓慢，阻碍了表型分析的实验过程。深度学习为自动化和解决关键植物表型研究问题提供了大量支持。基于机器学习的高通量表型分析是解决表型分析瓶颈的潜在解决方案，有望加速表型研究的实验周期。这项研究提出了一项研究，通过生成未来根和芽系统的分割掩模，深度网络预测植物预期生长的潜力。我们将现有的生成对抗性预测网络调整到这个新领域。结果显示了一个高效的植物叶子和根分割网络，该网络可以根据植物生长的时间序列数据，预测叶子和根系统在未来某个时间的样子。我们提供了拟南芥 (A. thaliana) 和芸苔 (Komatsuna) 植物的两个公共数据集的基准结果。实验结果表明所提出的方法具有很强的性能以及匹配专家注释的能力。所提出的方法对不同植物物种和突变具有高度适应性和可训练性（迁移学习/域适应）。
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 Arabidopsis (A. thaliana) and Brassica rapa (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.