# 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. nodestructive evaluation; garlic; snapshot multispectral imaging; heat stress; partial least square; partial least-squares discriminant analysis (PLS-DA); least-squares support-vector machines (LS-SVMs); deep neural networks (DNNs); convolutional neural networks (CNNs) +
Footnote †: journal: Medical Imaging  
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plants, ultimately affecting overall productivity [10; 11]. Studies have shown that rising temperatures caused a decrease in maize and wheat yields by 3.8% and 5.5%, respectively in 2011 [12]. In 2013, significant reductions in potato productivity were observed at temperatures above the optimum [13]. Drought and high temperatures also resulted in a 9-10% decline in grain production between 1964 and 2007 [14]. Furthermore, a 2017 study projected that a 1 \({}^{\circ}\)C increase in global mean temperature would lead to reductions in wheat, rice, maize, and soybean production by 6%, 3.2%, 7.4%, and 3.1%, respectively, highlighting the vulnerability of crops to climate change [15]. Consequently, heat stress caused by elevated temperatures adversely affects crop growth, necessitating research to mitigate its detrimental effects. Several studies have investigated the effectiveness of various nutrients, such as zinc, selenium, and boron, in alleviating heat stress [16; 17; 18]. Heat stress generates reactive oxygen species (ROS) that interfere with crop growth, but the application of boron can enhance the antioxidative activity of crops, minimizing the damage caused by ROS [16; 17; 18]. Superoxide dismutase (SOD), an antioxidant enzyme, plays a crucial role in mitigating ROS damage, and zinc influences SOD activity, making sufficient zinc intake beneficial for reducing the effects of heat stress [16; 19]. Selenium can enhance SOD activity and reduce ROS, protecting crops from the effects of heat stress [16; 20; 21; 22; 23]. Currently, precision agriculture is gaining momentum as an economically feasible and environmentally friendly approach. By utilizing crop-related data obtained from various sensors, precision agriculture enables the application of the required amount of inputs, such as fertilizers, thereby minimizing environmental impact and cost [24; 25]. Precision farming involves four key steps: (1) data acquisition on crops and their environment, (2) determining crop conditions and identifying suitable solutions based on acquired data, (3) prescribing appropriate actions for crops based on the identified solutions, and (4) analyzing the outcomes of the prescribed actions. Detecting high-temperature stress not only allows for proactive measures but also reduces labor and costs. Hence, studies have been conducted to detect high-temperature stress in crops to minimize its impact.