## 2 Materials and Methods  
The experiment was carried out during the 2023/24 harvest in the experimental area of the Federal University of Mato Grosso do Sul, Campus Chapadao do Sul, Mato Grosso do Sul, at the geographical coordinates 18\({}^{\circ}\)41\({}^{\prime}\)33\({}^{\prime\prime}\) south latitude and 52\({}^{\circ}\)40\({}^{\prime}\)45\({}^{\prime\prime}\) west longitude and at an average altitude of 810 m. The soil in the area was characterized as Latossolo Vermelho Distrofico [14] Clay (48% clay). The region's climate is defined as tropical savannah (Aw), according to the Koppen classification. The experiment was conducted in a strip scheme with thirty cultivars subjected to irrigated and rainfed management. Conventional sprinkling was used for irrigation, positioning the sprinklers every 12 m, thus creating an irrigated strip over all the hybrids with a width of 18 m (Figure 1). The method used was the FAO Penman-Monteith method, using data from an automatic weather station of the National Meteorological Institute (INMET), with 100% replacement of crop evapotranspiration (ETc) whenever the soil water balance approached the lower limit of the actual soil water capacity (ARWC), as in Ref. [15]. The soil was prepared using the conventional method (plowing and narrowing). The cultivars were sown using a seeder-adviser, with a rod-type furrowing mechanism for the fertilizer and a double mismatched furrowing mechanism for the seed, at a spacing of 0.45 m and with a population of 15 plants per meter. Three leaves per plot were collected 60 days after crop emergence, at full bloom, when soybeans reach their highest peak of nutrient absorption and photosynthetic activity, for evaluation by the hyperspectral sensor. The sampled leaves were read on the hyperspectral spectroradiometer sensor (FieldSpec 3 Jr, Analytical Spectral Devices, Boulder, CO, USA), which provides spectral information in the 350 to 2500 nm range. This made it possible to form a spectral signature for each species and differentiate them by their spectral curve. The spectral data was then separated into 28 spectral bands to reduce dimensionality, as performed in Ref. [16]. In this way, two types of databases were generated: one with all the spectral information provided by the sensor (WL) and one with the 28 spectral bands (SB). Each database was subjected to different machine-learning models to ascertain the improved accuracy of the models to distinguish the different eucalyptus species. The models tested were artificial neural networks (ANN), decision tree (DT), linear regression (LR), the M5P algorithm, and random forest (RF) [17; 10]. The machine-learning (ML) models were analyzed using stratified cross-validation 10 times with 10 repetitions (totaling 100 runs for each model). Correct classification (CC) and the Kappa coefficient were used to check the accuracy of the models. The machine-learning analyses were carried out with Weka 3.9.4 software, using the standard configuration for all the models tested [18] except for ANN, in which two layers with ten neurons in each were used. The artificial neural network (ANN) architecture used was of the multilayer perceptron (MLP) type, configured with a backpropagation algorithm to adjust the connection weights. The learning rate was set to 0.3, the momentum rate to 0.2, and the model was trained for 500 epochs. The J48 model, an implementation of the C4.5 classifier, was applied to solve classification problems, including an additional pruning step based on an error-reduction strategy. For this analysis, the pruning procedure was used, and the minimum number of instances allowed in a leaf node was set to 4. This configuration aims to minimize overfitting and improve generalization. REPTree operates with decision tree logic, creating multiple trees in several iterations and selecting the best one based on the information gained. It adopts error-reduction pruning as a splitting criterion to increase efficiency.