Snapshot-Based Multispectral Imaging for Heat Stress Detection in Southern-Type Garlic

Abstract

This study aims to develop a model for detecting heat stress in southern-type garlic using a multispectral snapshot camera. Raw snapshot images were obtained from garlic cloves during the garlic bulb enlargement period, capturing the visible (Vis) and near-infrared (NIR) regions. Image preprocessing was applied to obtain a 38-wavelength spectrum by combining a 16-wavelength image in the Vis region and a 22-wavelength image in the NIR region. These spectral data were then utilized to develop models, including PLS-DA, LS-SVM, DNN, and recurrence plots-based CNN (RP-CNN). On average, the LS-SVM model demonstrated the best performance in detecting heat stress during the garlic bulb enlargement period. This is attributed to the nonlinear nature of the spectral differences between groups caused by abiotic stress in garlic. The LS-SVM model is particularly effective at capturing such nonlinear relationships. Among the model images, LS-SVM yielded the best performance, followed by RP-CNN, DNN, and PLS-DA. Therefore, this study confirms the potential of snapshot-based multispectral imaging for measuring changes in garlic crops induced by high-temperature stress.