# Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques
## 1 Introduction
Numerous studies have demonstrated the effectiveness of Vis-NIR and NIR-based techniques for accurately identifying, distinguishing, and assessing the quality of fresh fruit in a fast and non-destructive manner [29; 30; 31; 32]. The effectiveness of hyperspectral imaging is supported by results from various studies; for example, one investigation demonstrated the ability of reflectance-mode HSI (400-1000 nm) to accurately monitor soybean flour content in pasta. Samples with increasing percentages of soybean flour (0-50%) were analyzed, and the FS-MRMR algorithm identified the most relevant wavelengths, with 655 nm proving particularly effective. A predictive model based on these wavelengths achieved high accuracy, confirming the potential of HSI for real-time quality control and its suitability for integration into industrial processes aligned with Industry 4.0 principles [33]. The use of hyperspectral imaging in the field of the non-destructive quality detection of fruit and vegetables has achieved significant results in recent years, enabling a detailed analysis of both the internal and external quality of the sample under examination [34]. Due to its ability to simultaneously examine the spectral and spatial characteristics of samples, it has proven particularly effective in assessing freshness, ripeness, the presence of defects, and other quality parameters of agricultural produce. The integration of this information enables producers, traders, and consumers to make decisions on product quality and marketing, helping to improve supply chain efficiency and ensure better consumer satisfaction [15]. Compared to the current state of the art, the innovation of this research lies in the identification of specific, optimized wavelengths for assessing the hydration status of baby leaf vegetables under real-field conditions. This is achieved through an integrated approach combining hyperspectral imaging and simplified mathematical modeling. The primary objective is to develop a reliable, rapid, and non-destructive method for determining leaf water content, meeting key requirements such as sample integrity, measurement repeatability, ease of implementation, and suitability for in-field application. The study aims to select the most influential wavelengths to accurately estimate leaf moisture levels, using them as input variables in a mathematical model that outputs the hydration status of the plant. In alignment with the Sustainable Development Goals (SDGs) and the Paris Agreement, this work contributes to a more sustainable use of freshwater resources by enabling irrigation practices that are tailored to the actual needs of the crop. From a precision agriculture perspective, the research supports the development of tools and technologies that enhance efficiency and sustainability in crop management. The real innovation concerns the possibility of obtaining machines capable of carrying out scouting operations, of monitoring the plant in the field in real time, with regard to health conditions and possible needs, such as water content, in order to prevent the product from reaching a stress phase that entails a series of disadvantages in terms of product quality, such as wilting, and lower product weight. To summarize, the research had the following main objectives:  
* Identify a rapid, reliable, and non-destructive method to analyze the different hydration levels in young leafy vegetables (baby-leaf), such as spinach. * Maximize irrigation efficiency and minimize waste, supporting more efficient production and reducing environmental impact. * Develop a model for the real-time monitoring of plant health conditions in the field, preventing water stress and preserving product quality. * Identify the specific wavelengths optimized by hyperspectral imaging to accurately estimate leaf moisture. * Contribute to the more sustainable use of water resources by promoting irrigation practices based on actual crop needs.