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预测建模,模式识别和模拟和控制环境中的植物生长的时空表示:全面的综述
Mohamed Debbagh1, Shangpeng Sun1, Mark Lefsrud1
1Department of Bioresource Engineering, McGill University, Montréal, Canada.
Plant phenomics (Washington, D.C.)
|December 19, 2025
概括
本综述涵盖了在现象学研究中用于植物生长预测的先进模式识别. 它强调了时空建模和动态环境相互作用,以准确预测.
科学领域:
- 植物生物学 植物生物学
- 计算建模计算建模
- 数据科学是数据科学.
背景情况:
- 准确的植物生长预测对于植物现象学研究至关重要.
- 模拟和控制环境需要强大的预测模型.
- 了解植物的时空特征和环境相互作用是关键.
研究的目的:
- 审查植物生长的最先进的预测模式识别技术.
- 检查确定性,概率和生成性建模方法.
- 讨论高通量表型化和预测中的应用.
主要方法:
- 探索回归和基于神经网络的预测模型.
- 对2D和3D结构化数据表示的分析.
- 检查功能结构植物模型和条件生成模型.
主要成果:
- 鉴定了当前基于实验的确定性方法的局限性.
- 强调需要采用包含不确定性和环境反的动态框架.
- 调查了时空建模和数据表示方面的进展.
结论:
- 未来的工作应该将领域知识与数据驱动方法相结合.
- 需要改进数据集和现实世界应用程序的实施.
- 动态的,意识到不确定性的模型对于准确的植物生长预测至关重要.
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