# Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models  
[PERSON]  
1 Department of Agronomy, Federal University of Mato Grosso do Sul (UFMS),  
Chapadio do Sul 79560-000, Mato Grosso do Sul, Brazil; [EMAIL_ADDRESS] (R.G.);  
[PERSON]ADDRESS] (L.P.R.T.); [EMAIL_ADDRESS] (P.E.T.)  
[PERSON]  
2 Graduate Program in Plant Production, State University of Sao Paulo (UNESP), Ilha Solteira,  
Sio Paulo 1538-000, Sao Paulo, Brazil; [EMAIL_ADDRESS] (D.C.S.);  
[EMAIL_ADDRESS] (M.F.C)  
[PERSON]  
2 Graduate Program in Plant Production, State University of Sao Paulo (UNESP), Ilha Solteira,  
Sio Paulo 1538-000, Sao Paulo, Brazil; [EMAIL_ADDRESS] (D.C.S.);  
[EMAIL_ADDRESS] (M.F.C)  
[PERSON]  
3 Graduate Program in Soil Science, State University of Sao Paulo (UNESP), Jaboticabal,  
Sio Paulo 14884-900, Sao Paulo, Brazil; [EMAIL_ADDRESS] (M.F.);  
[EMAIL_ADDRESS] (M.F.C)  
[PERSON]  
1 Department of Agronomy, Federal University of Mato Grosso do Sul (UFMS),  
Chapadio do Sul 79560-000, Mato Grosso do Sul, Brazil; [EMAIL_ADDRESS] (R.G.);  
larissa.r [EMAIL_ADDRESS] (L.P.R.T.); [EMAIL_ADDRESS] (P.E.T.)  
[PERSON]  
4 Department of Geography, State University of Mato Grosso (UNEMAT), Sinop 78555-000, Mato Grosso, Brazil 4472155473  
out on farms. Assessing phenotypic plant traits is a crucial step in the soybean genetic breeding programs, and thanks to advances in remote sensing and data analysis techniques, this process is becoming faster and more accurate [5]. The enhanced characterization achieved by remote sensing and evidenced by statistical modeling allows the understanding of several plant traits, even the most complex ones, assisting breeding programs in high-throughput phenotyping (HTP) [6].  
[PERSON] et al. [7] finds satisfactory results in differentiating soybean varieties using vegetation indices (VIs) and wavelengths obtained from UAV-based imagery. Spectral bands and VIs are positively correlated with several plant traits, such as leaf nitrogen content in corn, regardless of the variety analyzed [8]. HTP has assisted in monitoring the development of plants and their relationship with their environment [9].  
Combining machine learning (ML) with remote sensing becomes a prosperous approach in extracting information from agronomic traits, making data processing automated and more accurate [10]. This is because the use of ML enables the development of algorithms to be used in large datasets and with complex information (such as spectral imagery data) that requires integration among them [11].  
[PERSON] et al. [12], when using ML techniques combined with different VIs, achieved satisfactory results in predicting maize yields, with Random Forest (RF) standing out. [PERSON] et al. [13] used ML models applied to remote sensing data for soybean yield, in which Artificial Neural Networks (ANN) outperformed other algorithms.  
The hypothesis of our study is that using satellite imagery in data collection and ML in data processing can assist in the identification of soybean cultivars, making this process more accurate and faster. Therefore, the objectives were: (i) to verify which dataset input configuration (only spectral bands, only vegetation indices, or both) is most accurate in soybean cultivar discrimination, and (ii) to verify which ML technique is most accurate in this classification modeling.