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Updated: Jul 12, 2026

Evaluation of Photosynthetic Behaviors by Simultaneous Measurements of Leaf Reflectance and Chlorophyll Fluorescence Analyses
Published on: August 9, 2019
Design of a Chlorophyll Fluorescence Sensor Head for Continuous On-Leaf Measurements
Johannes Klueppel1, Samaneh Baghbani1, Stefan J Rupitsch1
1Department of Microsystems Engineering, Albert-Ludwigs-Universität Freiburg, Georges-Koehler-Allee 102, 79110 Freiburg, Germany.
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Continuous monitoring of physiological activity is increasingly important for environmental observation systems that track ecosystem responses to drought stressors. Chlorophyll fluorescence (ChlF) is a sensitive, early indicator of drought-induced stress in trees that is directly measured on leaves and needles. However, existing autonomous ChlF systems are typically bulky and disturb natural leaf movement or provide insufficient excitation intensity for reliable measurements. Here, we present a leaf-wearable sensor head specifically engineered for long-term, autonomous environmental monitoring in forests. The design integrates a high-intensity excitation interface based on a blue LED, delivering up to 9000 μmol m-2 s-1. The operation of the LED directly on the leaf enables energy-efficient excitation. To facilitate energy-aware design of field sensing systems, we introduce the metric photon density efficacy (μmol m-2 s-1 mW-1) for quantifying excitation efficiency in power-constrained fluorescence sensors. To ensure stable measurements under environmental forcing, the sensor head incorporates a 4.1 g lightweight Y-shaped magnetic attachment structure with a soft silicone interface, designed to maintain constant sensor-leaf geometry while allowing natural leaf motion. Mechanical characterization demonstrated a mean pull-off force of 3.41 N, while field tests confirmed reliable sensor operation under wind speeds up to 21.5 m s-1. The presented design enables the nonintrusive integration of optical sensors directly on leaves and needles for extended monitoring periods. This work establishes a new hardware approach for distributed leaf-level sensing within environmental monitoring networks, enabling high-resolution observation of vegetation physiological dynamics across spatial and temporal scales.

