# Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques
## 4 Conclusions  
The research focused on defining an effective method to discriminate the water status of spinach leaves, with a focus on the speed, ease of use, and non-invasiveness of the method. Through the mathematical models used, it is then possible to manage the water resource wisely.  
Precision mechanization, including the ability to monitor plant conditions in real time, is critical to ensuring their health and maximizing the quality of agricultural products.  
The initial water content in spinach appears to be crucial for ensuring its nutritional and commercial quality; however, its delicate nature and susceptibility to water loss make storage a challenge. Conventional methods for assessing their content are complex and costly; there is a need to turn to rapid, non-invasive approaches, and hyperspectral imaging emerges as a solution.  
Through the use of a neural network trained with absolute moisture data and reflectance spectra, it was possible to identify five significant wavelengths for estimating baby-leaf moisture. The robustness of the model was confirmed through several rounds of training and validation, showing a high degree of generalization and excellent correlation between predicted and measured data. The practical application of this model has shown surprising accuracy in predicting leaf moisture, as evidenced by the regression lines that match almost perfectly with the identical line in the graphs. These results indicate significant progress in leaf moisture analysis and prediction using advanced spectral analysis and artificial intelligence methods, with significant implications for agriculture and biological research. Further achievement was to have trained, validated, and tested a statistical classifier based on the K-NN model, using the same five wavelengths identified for the regression model, by which the high percentages of the correct classification of the data present in the two moisture classes were obtained. Spectroscopic analysis revealed that specific wavelengths in the visible and near-infrared (such as 565, 790, 890, 945, and 1465 nm) are closely related to the physiological and structural parameters in leafy plants. These bands show direct correlations with water content, chlorophyll, and structural characteristics, offering an effective means of monitoring plant health and freshness. This system may be useful in the early identification of the critical time of irrigation intervention. The developed system, by identifying an accurate threshold of 83% in foliar water content through advanced spectroscopic analysis, proves to be a valuable tool for the early identification of the critical time for irrigation intervention, with possible applications for precision irrigation.  
In the future, the application of these methods could extend to the testing of innovative agronomic techniques aimed at further improving the nutritional quality of produce through judicious management of water stress. This approach could be an important step toward more sustainable agriculture and the production of high-quality food.  
Conceptualization, R.R.; methodology, R.R., A.E.B., software, R.R.; validation, R.R.; formal analysis, R.R., A.E.B. and C.P.; writing--review and editing, R.R., C.P. and A.E.B.; founding acquisition, R.R.; supervision, R.R. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by Italian Ministry of University and Research (MUR), project \"Conservabilita, qualita e sicurezza dei prodotti ordrofrutitcoli ad alto contenuto di servizio--ARS01_00640--POFACS\", D.D. 1211/2020 and 1104/2021.  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflict of interest.