## 3 Results  
According to the hyperspectral signature of the soybean cultivars, there was a distinct behavior between irrigated and rainfed management (Figure 3), in which the reflectance factor of irrigated management showed lower values throughout the spectrum. Notably, the behavior of the curve in the visible region (VIS), which comprises the 400-700 nm wavelengths (the region highlighted in green in Figure 3), is almost imperceptible, except for the peak in the green range (550 nm). In the near-infrared (NIR) region, which covers the spectral range from 700 to 1300 nm (highlighted in red in Figure 3), and in the short-wave infrared (SWIR) region, between 1300 and 2500 nm (highlighted in blue in Figure 3), there is greater differentiation among the spectral curves. These wavelength ranges are of significant importance in differentiating spectral signals and are particularly relevant to improving the ability of machine-learning models to discriminate among different irrigation management in soybean cultivation. The high sensitivity of these spectral regions to variations in humidity, chemical composition, and leaf structure makes them essential for increasing the accuracy of the classification of different treatments. Figure 2: Schematic diagram of data collection and processing for the experiment. Figure 4 shows the spectral reflectance of the different bands for soybean management under rainfed and irrigated conditions. In all the bands analyzed, rainfed management had higher reflectance values than irrigated management. The distinction between the two managements is most evident in bands B21-B26, which predominantly cover wavelengths located in the near-infrared (NIR) region. This greater differentiation in the NIR region was also evident in the spectral curve shown in Figure 3, underscoring the sensitivity of this spectral band to the water content of the materials. Six machine-learning models were used to distinguish among soybean materials grown under rainfed and irrigated conditions, using two types of input data for each model: hyperspectral curve data (WL) and data from 28 selected spectral bands (SB). The performance of the models was assessed using three accuracy metrics. In the Kappa  
Figure 4: Spectral behavior of soybean materials under irrigated and rainfed management in the 28 spectral bands. Figure 3: Hyperspectral signature of soybean cultivars under irrigated and rainfed management. coefficient metric (Figure 5), the results showed that both the WL and SB inputs provided similar performances for the artificial neural network (ANN) and random forest (RF) models. On the other hand, the DT and J48 decision tree models, logistic regression (LR), and support vector machines (SVM) performed better when using all the spectral information from the hyperspectral curve (WL). When analyzing the models with the SB input, the ANN performed well, achieving an accuracy of 0.78. However, when using WL as the input, the SVM and RL models showed significantly better results, with accuracies of 0.99 and 0.96, respectively. There is little variation in the accuracy of both models, attributed to the smaller box plot. For CC (Figure 6), models also showed a similar behavior: the ANN and RF showed similar performances with both inputs, while DT, J48, RL, and SVM performed better when using WL. Using SB as the input, the ANN obtained an accuracy of 89.02%, while SVM and RL achieved 99.86% and 98.20%, respectively, when using WL, also showing little variation in the accuracy of both models. This is attributed to the smaller box plot reflecting greater accuracy due to the smaller variation in the response of the accuracy in the final results of the classification. When evaluating the F-Score metric (Figure 7), only for the ANN model did both inputs provide good accuracy. All the other models performed better using WL. Comparing the influence of each input on the models, ANN stood out as the best model using SB, achieving an accuracy of 0.89.