Kinetically Consistent Data Assimilation for Plant PET Sparse Time Activity Curve Signals

Abstract

Time activity curve (TAC) signal processing in plant positron emission tomography (PET) is a frontier nuclear science technique to bring out the quantitative fluid dynamic (FD) flow parameters of the plant vascular system and generate knowledge on crops and their sustainable management, facing the accelerating global climate change. The sparse space-time sampling of the TAC signal impairs the extraction of the FD variables, which can be determined only as averaged values with existing techniques. A data-driven approach based on a reliable FD model has never been formulated. A novel sparse data assimilation digital signal processing method is proposed, with the unique capability of a direct computation of the dynamic evolution of noise correlations between estimated and measured variables, by taking into explicit account the numerical diffusion due to the sparse sampling. The sequential time-stepping procedure estimates the spatial profile of the velocity, the diffusion coefficient and the compartmental exchange rates along the plant stem from the TAC signals. To illustrate the performance of the method, we report an example of the measurement of transport mechanisms in zucchini sprouts.

Code and data availability

The article describes a KC-DA data assimilation method for plant PET TAC signals with simulated and zucchini sprout PET data, but no public phenotype dataset, image/sensor data deposit, author analysis code, or trained model is mentioned. No data or code availability statement with a public URL appears in the supplied.

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