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SapFlower:一种自动化工具,用于使用深度学习预处理,填补缺口和分析液液流数据

Jiaxin Wang¹, Heidi J Renninger¹

¹Department of Forestry, Mississippi State University, Mississippi State, MS, 39762, USA.

The New phytologist

|April 9, 2025

概括

SapFlower自动化了热散散探头 (TDP) 液液流量数据的分析,大大降低了噪声,并以高准确度填补了空白. 这种工具提高了研究植物用水和生态系统动态的研究人员的效率和可访问性.

科学领域:

植物生理学植物生理学
生态系统科学生态系统科学
环境监测环境监测环境监测

背景情况:

使用散热探头 (TDP) 测量树脂流量对于了解植物用水和生态系统水循环至关重要.
TDP数据经常存在噪音,异常值和差距,需要大量的手动数据处理.
需要自动化和高效的数据分析工具来克服这些挑战.

研究的目的:

开发一个自动化工具,SapFlower,用于预处理和分析TDP的液液流数据.
整合先进的机器学习和深度学习模型,以进行可靠的数据清理和填补差距.
为研究人员提高树脂液流数据分析的效率和可访问性.

主要方法:

开发SapFlower,一个集成自动清洁算法的软件工具.
实现机器学习 (随机森林,高斯过程回归) 和深度学习 (LSTM,BiLSTM) 模型的数据处理.
自动化工作流程用于数据预处理,模型培训,填补差距,树脂木面积缩放和用水分析.

主要成果:

SapFlower有效地删除了90%以上的噪音数据,同时保留了真正的变化.
在填补差距方面实现了高精度,随机森林,LSTM和BiLSTM模型将RMSE降低到长期差距的10%或更低.
模型训练和预测是快速执行的,通常在几秒钟内.

结论:

SapFlower 显著提高了 TDP 汁液流量数据分析的效率和可访问性.
该工具使研究人员,即使是那些没有广泛的编程专业知识的人,也能够利用先进的分析技术.
未来的工作将包括对特定物种进行修正,并支持额外的测量方法.

关键词:

人口的人民 (Populus Populus) 是一个群体.生态系统的水平衡水平衡.硬木:硬木是一种坚硬的树木.机器学习是机器学习.植物用水使用水.随机的森林随机的森林经常性的神经网络.散热探测器进行散热.

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