Full-Wave Optical Modeling of Leaf Internal Light Scattering Dynamics with Potential Applications for Early Detection of Foliar Fungal Disease

Abstract

Light interacting with plant leaves undergoes reflection, transmission, scattering, and absorption, which together determine leaf optical properties. Changes in leaf architecture disrupt internal light scattering dynamics and consequently affect photosynthetic performance. Previous studies on internal leaf light scattering have primarily relied on ray-tracing approaches (e.g., Raytran) or radiative-transfer models (e.g., PROSPECT). However, these high-frequency approximations cannot capture diffraction and coherent multiple scattering in wavelength-scale leaf tissues, unlike full-wave electromagnetic simulations. Here, we employ GPU-accelerated Finite-Difference Time-Domain (FDTD) simulations to model internal light scattering dynamics using segmented cross-section image geometries of representative dicot and monocot leaves with wavelength-dependent complex refractive indices. The simulations accurately reproduce the reflectance and transmittance characteristics of healthy leaves, showing strong agreement with the PROSPECT model, with average Lin's concordance values of 0.8962 for dicot leaves and 0.7849 for monocot leaves. We further simulate early-stage necrotrophic fungal infection by modeling melanized hyphae penetrating the cuticle and upper epidermis. Diseased leaves exhibit a pronounced reduction in visible green reflectance and a marked suppression of the near-infrared reflectance plateau, consistent with experimental observations. Remaining discrepancies in the visible band are expected to be reduced through more advanced geometric and material modeling. This proof-of-concept study presents a full-wave FDTD optical modeling framework for plant-leaf light scattering, enabling physics-based analysis of internal scattering before and after early-stage fungal penetration and supporting the use of light scattering as an indicator for pre-symptomatic plant fungal disease detection.