# Snapshot-Based Multispectral Imaging for Heat Stress Detection in Southern-Type Garlic
## 5 Conclusions  
In this study, we developed an optimal model for detecting high-temperature stress in southern-type garlic during the garlic bulb enlargement period using a multispectral snapshot camera. The multispectral snapshot camera is capable of capturing 16 wavelengths in the visible region (462-624 nm) and 25 wavelengths in the near-infrared region (603-871 nm), providing a total of 41 wavelength information. Since the data from the visible and near-infrared wavelengths are mixed in the raw images obtained by the multispectral snapshot camera, preprocessing steps are necessary to separate each wavelength and remove the background in order to obtain the garlic spectrum. We employed PLS-DA and LS-SVM as machine learning models, and DNN and CNN as deep learning models. The LS-SVM model showed the best overall performance, with a small difference between the accuracy of the calibration model and the accuracy of the prediction model, and relatively higher accuracy than other models. This can be attributed to the nonlinear nature of the differences between groups in garlic crops under high-temperature conditions. The LS-SVM model effectively captures these nonlinear relationships. On the other hand, the DNN and RP-CNN models did not outperform the LS-SVM model, mainly because the depth of their layers was not sufficient. Although there was no significant difference in performance between the DNN and RP-CNN models, the RP-CNN model has the potential to show significant improvement by using deeper layers, as it incorporates not only data but also information about the relationships between data. Despite the rigorous evaluation process, there are certain limitations and potential biases that should be acknowledged. Firstly, the evaluation was performed on a specific dataset collected under controlled experimental conditions. The generalizability of the models to different environments and variations in data collection protocols may vary. Secondly, the choice of performance metrics and evaluation criteria is subjective to some extent and can impact the interpretation of results. It is important to consider the specific goals and requirements of the application when selecting the best model. However, it is crucial to acknowledge the limitations and potential biases in the evaluation process, such as dataset-specific considerations and hyperparameter selection. Further research and validation on diverse datasets are necessary to ascertain the models' applicability and generalizability to real-world agricultural scenarios. The findings of our study have significant implications for practical applications in agriculture, particularly in the detection of heat stress and the effective management of garlic crops using snapshot-based multispectral imaging. By leveraging the benefits of multispectral imaging technology, farmers can enhance their ability to detect heat stress in garlic crops and implement timely interventions for better crop management. Firstly, the use of snapshot-based multispectral imaging allows for non-destructive and rapid assessment of heat stress in garlic crops. This technology enables the simultaneous capture of multiple narrow spectral bands, providing valuable information about the physiological status of the plants. By analyzing the spectral responses across different wavelengths, farmers can identify specific indicators or patterns associated with heat stress, such as changes in chlorophyll content, water content, or metabolic activity. This knowledge can aid in early detection and intervention, allowing farmers to mitigate the detrimental effects of heat stress on garlic crops. Secondly, snapshot-based multispectral imaging provides spatial information along with spectral data, enabling the identification of localized areas affected by heat stress. By mapping the spatial distribution of stress indicators, such as temperature gradients or variations in vegetation indices, farmers can precisely target the affected regions for remedial actions.