# Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits
## 6 Conclusions  
Hyperspectral (HS) imaging and multispectral (MS) imaging are promising measurement techniques to monitor the growth status of plants. Their feasibility, nondestructive nature, and rapid inspections make HSI/MSI suitable for large-scale plant factories. Additionally, monitoring using a manual technique, i.e., cutting, can damage the plant body, causing physical or mechanical stress, and resulting in a lower yield production. Furthermore, HSI/MSI can be used to observe plant features that cannot be detected with naked human eye, such as chemical compounds. Most of the research studies emphasized that crops were affected by the controlled surrounding conditions, such as abiotic and biotic stresses. Subsequently, HSI/MSI were utilized to monitor the plant growth status.  
The spectral data acquired using HSI/MSI were then coupled with chemometric analyses. However, to remove noises from the observation, several preprocessing methods could be performed. Chemometric techniques were used for predicting plant chemical composition and classification, among many other functions. Most researchers used VIs to distinguish plants' response in a given controlled environment. In recent years, deep learning analysis methods have been widely used to assist HSI/MSI. Further, the input variables (spectral information) are then convoluted and pooled to obtain the results. Moreover, an obtained image can also be processed (image processing) to identify plant symptoms during the growth period.  
The applications of HSI and MSI for monitoring crops' growth status may also vary depending on the aim. Firstly, applications, such as detection of heavy metals in plant tissues as a side effect of pollution, can also be conducted to safeguard human health and food safety. Secondly, the effects of biotic infection during growth season can also be observed. Various types of plant stresses can impact on the subsequent agri-food production lines, such as storage time. However, there is still limitation in research on the use of HSI/MSI for detecting changes in specific chemical compounds under a given stress treatment. Furthermore, a spectral selection algorithm needs to be developed and employed to choose the most representative waveband and increase model robustness and performance.  
Conceptualization, H.L. and J.L.; methodology, R.A.P.H. and H.L.; validation, H.L.; writing--original manuscript preparation, R.A.P.H.; writing--review and editing, R.A.P.H., J.L. and H.L.; supervision, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through Open Field Smart Agriculture Technology Short-term Advancement Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (grant number: 322032-3).  
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The authors gratefully acknowledge [PERSON] for her assistance in editing the picture illustrations.  
The authors declare no conflict of interest.