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Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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Related Experiment Video

Updated: Jun 29, 2025

A Rapid Laser Probing Method Facilitates the Non-invasive and Contact-free Determination of Leaf Thermal Properties

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Physics-assisted machine learning for THz time-domain spectroscopy: sensing leaf wetness.

Milan Koumans¹, Daan Meulendijks¹, Haiko Middeljans¹

¹Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands.

Scientific Reports

|March 26, 2024

Summary

This summary is machine-generated.

Machine learning models, including decision trees and convolutional neural networks, accurately determine leaf wetness using terahertz (THz) time-domain spectroscopy. This advancement aids in predicting plant diseases by quantifying water on plant leaves.

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Area of Science:

Physics
Plant Science
Data Science

Background:

Terahertz (THz) time-domain spectroscopy (TDS) requires advanced signal processing for practical applications.
Leaf wetness is a critical factor in plant disease development and prediction.
Machine learning (ML) offers potential for analyzing complex spectroscopic data.

Purpose of the Study:

To apply ML techniques to THz-TDS data for quantifying leaf wetness.
To integrate domain knowledge of light-matter interactions into ML models.
To assess the generalizability of ML models for agricultural applications.

Main Methods:

Acquisition of 12,000 THz-TDS transmission spectra from distinct water droplet patterns on a plastized leaf.
Application of decision trees and convolutional neural networks (CNNs) with physics-motivated feature selection.
Evaluation of model performance on datasets with varying deviations from training data.

Main Results:

ML models demonstrated effectiveness in determining leaf wetness from THz-TDS data.
Physics-informed choices improved model accuracy and interpretability.
CNNs showed promise in generalizing to unseen water droplet patterns.

Conclusions:

ML techniques, enhanced by domain knowledge, can significantly advance THz-TDS for agricultural applications.
Accurate leaf wetness detection using THz-TDS and ML can aid in plant disease management.
Further research into model generalizability is crucial for real-world deployment.