###### Abstract  
The objectives of this work are (i) to classify soybean cultivars under different irrigation managements using hyperspectral data, looking for the best machine-learning algorithm for the classification and the input that improves the performance of the models. The experiment was implemented in 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, and it was conducted in a strip scheme with seven cultivars subjected to irrigated and rainfed management. Sixty days after crop emergence, three leaves per plot were collected for evaluation by the hyperspectral sensor. The spectral data was then separated into 28 bands to reduce dimensionality. In this way, two 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 in distinguishing the different eucalyptus species. The models tested were artificial neural networks (ANN), decision trees (DT), linear regression (LR), MSP algorithm, random forest (RF), and support vector machine (SVM). The results demonstrate the effectiveness of machine-learning models in differentiating soybean management under rainfed and irrigated conditions, highlighting the advantage of hyperspectral data (WL) over selected spectral bands (SB). Models such as the support vector machine (SVM) showed the best levels of accuracy when using the entire available spectrum. On the other hand, artificial neural networks (ANN) performed well with spectral band data, demonstrating their ability to work with smaller data sets without compromising the classification. algorithms; spectral bands; vegetation indices; computational intelligence; _Glycine max_2024, 17, 542. [[https://doi.org/10.3390/a1712054202420242024202420242024202420](https://doi.org/10.3390/a1712054202420242024202420242024202420)]([https://doi.org/10.3390/a1712054202420242024202420242024202420](https://doi.org/10.3390/a1712054202420242024202420242024202420))for the greatest productive losses in soybean cultivation due to its relationship with the decrease in photosynthetic performance and, consequently, its damage to productivity [3]. Water availability is considered one of the most important productivity factors due to the greater resistance to diffusion of CO entering the cells when less water is available, which results in a reduction in the internal concentration of CO and a decrease in net photosynthesis and soybean yields [4]. To mitigate the effects of water deficit, irrigation is a widely used technique since the adequate supply of water via irrigation is capable of providing better crop development, better fertilization efficiency, plant health control, and higher productivity. However, genetics influences the agronomic characteristics of the crop, as cultivars with different phenological characteristics can show different responses to irrigation [5]. Technological advances applied to agriculture make it possible to use hyperspectral sensors to measure different characteristics. Hyperspectral sensors are remote sensing tools that enable the acquisition of hundreds of spectral bands, reproducing the reflectance spectra of the targets and allowing them to be identified [6]. Reflectance is an analytical technique that uses the electromagnetic energy reflected by materials in the visible, near-infrared, and short-wave infrared regions through electronic and vibrational processes where certain atoms and molecules absorb energy depending on their atomic structures, manifesting them through spectral reflectance curves [7]. In this way, it is possible to capture small changes in the chemical composition of the materials by detecting the shifting of the position and shape of the spectral absorption features of the reflected radiation [8].