## 4 Discussion
The compilation of the use of ML with spectral data hasbeen explored in the analysis of soybean seeds, as it is a non-destructive method used in the identification of varieties [11] or in the classification of vigor [12], in addition to being able to be used in the industrial classification of soybean grains. Furthermore, the application of these techniques can be extended to the identification of diseases in plants in terms of severity [35; 36] and the identification of the causative agent [37]. The SVM model is a supervised machine-learning algorithm used in classification and prediction tasks that is widely used for its pattern recognition efficiency [38]. The main objective of the algorithm is to find the best way to effectively separate data into categories, with high accuracy and requiring less memory than other classification methods, especially when applied to high-dimensional datasets such as those with many variables or characteristics, for example, hyperspectral data. This makes SVM a robust choice for classification tasks in complex problems [39; 40]. SVM achieved good performance in classifying individual species, even though these species are closely related and morphologically similar, highlighting the model's ability in complex classification tasks because restricted morphological differences make differentiation difficult. Another sign of effectiveness reported about SVM is its ability to distinguish levels of psyllid attack severity in eucalyptus, especially when using all the information provided by the spectrum [17]. The WL input also provided better overall accuracies for all the models used to distinguish irrigated and non-irrigated soybean materials, because providing all the spectrum information made available by the sensor provides a complete set of data that contributes to improving the accuracy of the models by increasing the amount and variability of data available for analysis. Providing full spectrum information makes variations available in different wavelength ranges, each providing data on specific characteristics of the material being evaluated [41]. By making the entire spectrum available as information to ML models, they have a greater amount of information with more details that help distinguish the patterns of variation among the classes studied. In some studies, the limited availability of spectral information can change the performance of the models, and then the use of artificial neural networks (ANN) becomes an efficient alternative, especially when the data is reduced to bands, as demonstrated in the accuracy of the models tested (Figure 3). This is because neural networks are capable of learning non-linear patterns, guaranteeing good performance in a variety of problem-solving situations, even when the data available is not as detailed [42]. ANNs have many applications, especially in artificial intelligence, using this model for automatic control, information processing, and pattern recognition [43]. Ref. [44] using ANN models, achieved a high correlation coefficient between estimated and observed corn grain yield and also showed acceptable errors in yield estimation, making it the best model for predicting harvest area and soybean production [45]. Our findings highlight the importance of exploiting high-resolution spectral data, such as that provided by hyperspectral curves, to maximize the accuracy of classification models in agronomic research. The clear distinction between irrigation management in the spectral curve and among bands, especially in the NIR bands, highlights the importance of using data from this spectral region to identify management variations, reinforcing its applicability in monitoring and decision-making in the field. Thus, the adoption of machine-learning techniques associated with the use of hyperspectral data can provide a specialized methodology to optimize agricultural practices and increase management efficiency in soybean crops.