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Related Experiment Video

Updated: Jan 7, 2026

Using Flatbed Scanners to Collect High-resolution Time-lapsed Images of the Arabidopsis Root Gravitropic Response

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Bayesian adaptive sampling: A smart approach for affordable germination phenotyping.

Félix Mercier¹, Nizar Bouhlel², Angelina El Ghaziri²

¹LARIS, Université d'Angers, Angers, France.

Plant Phenomics (Washington, D.C.)

|December 19, 2025

Summary

This study introduces an adaptive sampling method for digital phenotyping to reduce data costs. Markov chain Monte-Carlo (MCMC) sampling offers the best balance of data compression and accuracy for temporal monitoring.

Keywords:

Adaptive Sampling Bayesian methods Low Cost phenotyping Seed Germination

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A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

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Published on: August 5, 2020

Area of Science:

Digital phenotyping and computational biology.
Data science and machine learning applications.
Agricultural technology and plant science.

Background:

Digital phenotyping generates vast amounts of temporal data, increasing processing and storage costs.
Efficient data sampling is crucial for managing large-scale digital phenotyping datasets.
Bayesian inference provides a framework for optimizing data acquisition in dynamic systems.

Purpose of the Study:

To develop and evaluate an adaptive sampling method for optimizing data collection in digital phenotyping.
To reduce the costs associated with data production, processing, and storage in temporal monitoring.
To compare the performance of five different Bayesian inference methods for adaptive sampling.

Main Methods:

Proposed an adaptive sampling method based on Bayesian inference, utilizing historical data and predictive models.
Assessed five Bayesian methods: Importance Sampling (IS), Markov Chain Monte Carlo (MCMC), Gaussian Process (GP), Extended Kalman Filtering (EKF), and Sampling Importance Resampling particle filtering (SIR-PF).
Evaluated methods based on compression rate, data distortion, and computational cost in monitoring germination rates.

Main Results:

Markov Chain Monte Carlo (MCMC) demonstrated the best trade-off, achieving a compression rate of 0.2 with minimal distortion.
Gaussian Process (GP) provided unbiased parameter estimation and adaptability to varying germination speeds, with reasonable computational times.
All tested Bayesian methods showed potential for optimizing sampling in digital phenotyping applications.

Conclusions:

Adaptive sampling using Bayesian inference significantly reduces data management costs in digital phenotyping.
MCMC and GP are promising methods for efficient temporal monitoring, offering different strengths in compression, accuracy, and adaptability.
This approach enables more sustainable and cost-effective large-scale digital phenotyping studies.