## 1 Introduction  
Genetic improvement has undergone significant advances in plant gene analysis over the years, but phenotypic evaluation techniques still need to be improved in search of faster and more accurate responses [1]. Currently, the methods used for phenotyping plants are costly, time-consuming, and subjective, and most require the plant to be destroyed [2; 3]. High-throughput phenotyping (HTP) is a faster and non-destructive approach to the evaluation of plant characteristics, making it an essential tool in agriculture [4].  
Using sensors attached to unmanned aerial vehicles (UAVs) enables fast, cost-effective, and high-resolution image processing, contributing to the measuring and monitoring of plant characteristics in several crops [5; 6; 7]. Furthermore, crop information is collected simultaneously at a large scale over time and space, making it possible to carry out automated data analysis [8].  
Among the uses of phenotyping, we can highlight the identification and differentiation of cultivars by using multispectral sensors, which can be used to detect errors in souring and find out the most differentiating characteristics between cultivars [9], thus contributing to better targeting of breeding programs. Using UAVs for phenotyping crops such as sorghum can make it easier to measure plant height, for example, since some genotypes are too tall, which hinders conventional assessments [10]. Other applications include the determination of crop chlorophyll [11] and, as it is a non-destructive evaluation, it enables the assessment of the presence of green leaves after flowering [12].  
The use of HTP generates a large amount of data in which the use of machine learning techniques is a means of processing such data effectively and accurately, which aims to use algorithms to relate the information extracted to the reflectance of the phenotypes obtained [13]. To the best of our knowledge, there is some research that demonstrates that it is possible to distinguish different tree species using UAV-multispectral sensing in conjunction with machine learning models [14]. For genetic materials from the same species, there is little research on soybean cultivation [15]. The biochemical properties of leaves affect the absorption and reflectance of light in various spectral bands [16]. Spectral signatures can reveal biochemical and structural differences between plant populations distributed throughout space. Such physiological differences in populations of the same species can be attributed to the genetic distinction between them [17].  
Distinguishing sorghum hybrids according to their canopy reflectance and using machine learning algorithms to classify these hybrids is a novel strategy that has yet to be studied. Our hypothesis is that using UAV imaging and machine learning techniques for data processing enables faster, accurate and large-scale discrimination of sorghum hybrids. Thus, the aim of this work was to: (i) discriminate sorghum hybrids using canopy reflectance at different wavelengths and vegetation indices; (ii) evaluate the performance of machine learning algorithms in classifying sorghum hybrids; and (iii) assess the accuracy of different input variables in machine learning models.