# Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques
## 1 Introduction
It is possible to take an approach that significantly considers crucial factors such as rainfall, evapotranspiration, soil moisture, and crop water requirements, based on the use of vegetation indices that provide information on plant health and water status using the measurements obtained from remote sensors or field instruments. Although such indices offer the advantage of computational simplicity, they do not fully account for the essential variables related to crop needs: consequently, they may not be able to adequately represent agricultural drought [12]. The increasing consumer acceptance of baby leaf vegetables underscores the critical importance of meticulous water management during their cultivation, directly impacting their water content and their quality and shelf-life. Baby leaf vegetables are a production type that is gaining increasing consumer acceptance [13]. Spinach appears like a strategic crop of particular interest to the processing industry, its crop cycle following all other spring-summer cycle species, occupying a time slot in which no other crops are present [14]. The commercial successes of these products is due to several factors, including the service offered to increasingly busy consumers, the reduction in waste due to the complete consumability of the product and the high quality associated with this type of product in terms of appearance, taste, and nutritional value [13; 14]. It is essential to detect the freshness of spinach to safeguard its quality and commercial value. Traditional detection methods involve a number of complex procedures, such as measuring chemical indices such as moisture, chlorophyll, and soluble sugar content; however, these methods are time consuming and require the supervision of experienced operators, in addition to being costly. Therefore, the need to develop a rapid and non-invasive method to detect the freshness of vegetables, including spinach, is crucial. This approach would allow high-quality standards to be maintained throughout the distribution chain, while ensuring excellent nutritional quality and increased consumer satisfaction [15]. Leaf water content is one of the most common physiological parameters that limit the efficiency of photosynthesis and biomass productivity in plants, so it is of great importance to determine or predict water content quickly and non-destructively [16]. The possibility to regulate irrigation levels with extreme precision could increase the nutritional quality of produce through targeted agronomic practices. Baby-leaves such as spinach are rich in antioxidant compounds that are easily perishable in post-harvest. It has been observed that irrigation management can influence the permanence of these substances over time. Studies were conducted on the effects of water stress on antioxidant compounds in spinach. The study showed that, compared to the control thesis, water stress significantly slowed down the loss of ascorbic acid in spinach leaves at the post-harvest stage [17]. Various approaches have been developed to estimate water content through the analysis of reflectance data. Several studies have explicitly tested the performance of different vegetation indices and have shown that stronger absorbance bands are more sensitive to changes in leaf water content than fainter water absorption bands in the near-infrared region [18]. As reported in the study conducted by [PERSON] et al. [19], hyperspectral data acquisition combined with multivariate statistical analysis and the creation of customized indices made it possible to study the relationship between reflectance and physiological/biochemical parameters in stressed olive trees. The study used hyperspectral reflectance to estimate the health of stressed olive trees (drought/salinity), analyzing varietal tolerance (photosynthesis, water, enzymes), correlation between spectral data and physiological parameters for future drone scans, and identifying wavelengths for customized enzymatic indices.