Coherent terahertz laser feedback interferometry for hydration sensing in leaves.

Area of Science:

• Agricultural Science
• Optics and Photonics
• Biophysics

Background:

• Terahertz (THz) radiation's sensitivity to water content positions it as a valuable tool for agricultural hydration monitoring.
• Accurate hydration assessment is crucial for optimizing crop yield and managing plant health in agriculture.

Purpose of the Study:

• To investigate the feasibility of using terahertz sensing for monitoring the hydration levels of freshly harvested Celtis sinensis leaves.
• To develop and apply a machine learning model for predicting relative water content from terahertz images.

Main Methods:

• An imaging platform utilizing quantum cascade lasers and laser feedback interferometry was employed to capture high-resolution terahertz amplitude and phase images.
• Wide-angle imaging at high frame rates allowed for the observation of dynamic water transport within the leaves.
• Complementary image data was processed using a machine learning model to predict relative water content.

Main Results:

• The terahertz imaging platform successfully generated detailed images of leaf hydration.
• The machine learning model accurately predicted changes in hydration levels over time from single image frames.
• The study confirmed the potential of terahertz sensing for dynamic water transport monitoring.

Conclusions:

• Terahertz sensing, combined with machine learning, offers a promising non-destructive method for monitoring plant hydration in agricultural settings.
• This technique can provide real-time insights into water dynamics within plant tissues, aiding in precision agriculture.
• Further development could lead to advanced tools for optimizing irrigation and managing plant stress.