## 4 Discussion  
The ability to identify and classify plants is essential for automatic vegetation mapping, as it is an effective and accurate technique for comprehensively classifying plants, which is currently in high demand [19]. In this regard, the use of data from sensors, especially those that provide hyperspectral reflectance of leaves, provides a series of narrow and continuous spectral bands, enabling finer discrimination of the spectral properties of plants [19; 20; 21]. What determines the spectral distinction among plants is the biochemical composition, pigments, water content, and internal cell structures [22]. When the plant goes through periods of water restriction, various physiological mechanisms are negatively affected [23], and stomatal closure occurs to prevent water loss. The process of avoiding water loss leads to a reduction in the absorption of carbon dioxide from the air [24; 25] and consequently affects the absorption of nutrients by soil roots [26]. These effects caused by water stress affect the plant instantaneously, with a reduction in leaf water content that interferes with gas exchange, as previously mentioned, and in the long term, significantly affects the morphological characters of the crop, such as the leaf area index and plant biomass [27; 28; 29]. This influences what will be reflected by the plant. Using sensor information is an efficient alternative for monitoring water relations through canopy and leaf reflectance, thus helping with water management, especially in precision agriculture practices. [30]. Specifically, leaf reflectance in the shortwave infrared (SWIR) region (1300-2500 nm) is strongly influenced by leaf structure and water content [31]. Ref. [32] state that leaf reflectance and leaf water content in maize have strong correlations in the transition between the NIR and SWIR wavelengths. Ref. [33] stated that VIS and SWIR wavelengths have greater sensitivity to leaf water content compared to NIR. Figure 1 shows that the greatest distinction among the curves formed by the soybean genetic materials under irrigated and rainfed management is in the NIR and SWIR regions, while in the VIS region, there was a greater distinction among the curves in the green region corresponding to 560 nm, suggesting that this band may also play an important role in distinguishing water management. The regions of the highest reflectance, at 970 nm and 1200 nm, provide convenient wavebands for detecting the water content of the plant canopy [34]. Specifically, these regions are close to the B24 and B25 bands, which cover wavelengths of 960 nm and 1100 nm, respectively. These bands, as shown in Figure 4, showed a significant distinction between rainfed and irrigated management, reinforcing the usefulness of these bands in crop water monitoring. The sensitivity of these bands to variations in water content makes them essential for implementing water management strategies in agricultural systems, allowing for a better distinction between different irrigation treatments. Once the spectral regions that make it possible to distinguish water management have been identified, such as the bands covering the NIR and SWIR, the potential for using this information in advanced machine-learning models becomes clear. Machine learning has become a highly relevant alternative for obtaining recognition, prediction, and filtering results in many complex agricultural problems. The significant distinction between irrigation managements in the ranges indicates that spectral data, when applied to supervised machine-learning algorithms, can be extremely effective for the automated classification of genetic materials under different water regimes. The use of RL and SVM models stands out, specifically SVM, which, even though it showed no statistical difference, had a higher classification rate.