Denaro, Lino GardaLino GardaDenaroSyariz, Muhammad AldilaMuhammad AldilaSyarizHuang, Cho-yingCho-yingHuang2026-01-232026-01-232025-11-01https://www.scopus.com/pages/publications/105024196725https://scholars.lib.ntu.edu.tw/handle/123456789/735563The use of hyperspectral reflectance and transmittance data, as well as advanced machine learning approaches, has substantially improved material analysis by expanding into the near-infrared (NIR) and shortwave infrared (SWIR) areas beyond the visible spectrum. This study looks into fresh green tea leaves' SWIR-2 region (2001-2500 nm), which is important for materials science, agriculture, and environmental monitoring applications. While hyperspectral data contains abundant and detailed information, its complexity and high dimensionality pose difficulties for precise analysis. We use deep learning methods, particularly convolutional neural networks (CNNs)-based dual-input/output (Dual-Net), to address these issues by refining spectral signatures and improving prediction accuracy across a variety of applications. Our method accounts for spectral dependencies, improving the accuracy of predictions concerning leaf attributes like water content, nitrogen and chlorophyll concentrations, and other biophysical properties, even under complex environmental conditions. The findings show that deep learning models make effective use of SWIR-2 spectra for important evaluations of plant health. The proposed method produced mean (±SD) root-mean-square errors (RMSEs) of 0.012 (±0.012) of reflectance and 0.011 (±0.010) of transmittance for individual spectra, and RMSEs of 0.028 (±0.029) of reflectance and 0.022 (±0.023) of transmittance for SWIR-2 bands. This method shows strong potential for advancing remote sensing applications, particularly in monitoring plant health and productivity. Further improvements could be realized through the development of a more comprehensive green leaf spectral database.journal article10.1088/1755-1315/1551/1/012006