1：Experimental Design and Method Selection:

The study employs hyperspectral imaging and time-resolved fluorescence imaging combined with multivariate analysis techniques such as partial least squares regression (PLSR) and principal component analysis (PCA) for non-destructive monitoring.

2：Sample Selection and Data Sources:

For wood monitoring, European beech and Scots pine samples (10 total) with specific dimensions were used, immersed in water and air-dried. For leaf stress monitoring, potted Epipremnum aureum plants were used under controlled conditions with water stress applied.

3：List of Experimental Equipment and Materials:

Hyperspectral camera (Hyspex VNIR-1600, Norsk Elektro Optikk), CCD video camera module (XC-ST50, Sony), band-pass filter, excitation lights at 470 nm and 660 nm, thermostatic chamber.

4：Experimental Procedures and Operational Workflow:

For wood, hyperspectral images were acquired seven times during drying, with weight measurements; PLSR models were built. For leaves, time-resolved CFI images were captured at 3 frames per second for 20s after light irradiation, and PCA was applied.

5：Data Analysis Methods:

PLSR for moisture content prediction, PCA for analyzing time dependency of fluorescence intensity, with metrics like RPD for accuracy.