arXiv subjects

Da-Young Lee

Publications and source records attributed to Da-Young Lee.

3 recordsLinked to original sources

arXiv2025

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

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.

physics.bio-ph↗

arXiv2022

Leaf Tar Spot Detection Using RGB Images

Tar spot disease is a fungal disease that appears as a series of black circular spots containing spores on corn leaves. Tar spot has proven to be an impactful disease in terms of reducing crop yield. To quantify disease progression, experts usually have to visually phenotype leaves from the plant. This process is very time-consuming and is difficult to incorporate in any high-throughput phenotyping system. Deep neural networks could provide quick, automated tar spot detection with sufficient ground truth. However, manually labeling tar spots in images to serve as ground truth is also tedious and time-consuming. In this paper we first describe an approach that uses automated image analysis tools to generate ground truth images that are then used for training a Mask R-CNN. We show that a Mask R-CNN can be used effectively to detect tar spots in close-up images of leaf surfaces. We additionally show that the Mask R-CNN can also be used for in-field images of whole leaves to capture the number of tar spots and area of the leaf infected by the disease.

cs.CV↗

arXiv2021

Digital image quantification of rice sheath blight: Optimized segmentation and automatic classification

Rapid and accurate phenotypic screening of rice germplasms is crucial in screening for sources of rice sheath blight resistance. However, visual and/or caliper-based estimations of coalescing, necrotic, ShB disease lesions are time-consuming, labor-intensive and exposed to human rater subjectivity. Here, we propose the use of RGB images and image processing techniques to quantify ShB disease progression in terms of lesion height and diseased area. To be specific, we developed a pixel color- and coordinate-based K-Means Clustering (PCC-KMC) algorithm utilizing Mahalanobis metric aimed at accurate segmentation of symptomatic and non-symptomatic regions within rice stem images. The performance of PCC-KMC was evaluated using Lin's concordance correlation coefficient by comparing its results to visual measurements of ShB lesion height and to lesion/diseased area measured using ImageJ. Low bias and high precision were observed for absolute lesion height (bias=0.93, precision=0.94) and absolute symptomatic area (bias=0.98, precision=0.97) studies. Moreover, we introduced a convolutional neural network (CNN) for the automatic annotation on clusters, termed PCC-KMC-CNN. Our CNN was trained based on 85%:15% of composition for training and testing dataset from total 168 ShB-infected stem sample images, recording 92% accuracy and 0.21 loss. PCC-KMC-CNN also showed high accuracy and precision for the absolute lesion height (bias=0.86, precision=0.90) and absolute diseased area (bias=0.99, precision=0.97) studies. These results demonstrate that the present methodology has great potential and promise to substitute the traditional visual-based ShB disease severity assessment.

q-bio.QM↗