# Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models
## 4 Discussion
### Tested Inputs  
The use of remote sensing for measuring soybean agronomic traits has great potential to revolutionize genetic breeding programs and production systems, especially because this technology allows the quantification of phenotypic variables by combining images [10]. Traditional genotype selection programs are limited to costly and imprecise field analyses, which can be improved using remote sensing technologies [5]. This technology  
Figure 6: Confusion matrix for discrimination of five soybean cultivars using artificial neural networks (ANNs) and different inputs (vegetation indices—VIs, spectral bands—SBs, and SBs + VIs).  
demonstrates efficiency in classifying soybean varieties, as previously reported by [PERSON] et al. [7; 36].  
When evaluating the inputs within each model, SBs and SBs + VIs obtained the highest means for correct classification and Kappa coefficient. Even though both inputs have achieved similar results, in a practical way, using the SBs would be more feasible since to obtain them, it is not necessary to perform calculations such as those used in the acquisition of the VIs.  
Spectral bands are a reliable source for spatial and temporal detailing, making estimates on variables such as chlorophyll and leaf area index in agricultural crops [37]. [PERSON] et al. [38] have achieved accurate responses using spectral bands in discriminating eucalyptus plants for different levels of boron fertilization.  
In addition to the results exposed in the breakdowns for correct classification and Kappa coefficient, the correlogram showed a significant relationship between spectral bands and variables. These results demonstrate the efficiency of using the spectral bands B1, B2, B3, B4, B6, and B7 to identify soybean cultivars. Using more than one spectral band when processing the analyses, a detailed exploration of what is being evaluated is possible, providing relevant information about the differentiation of soybean varieties [7; 39].  
Tables 2 and 3 show the efficiency of using spectral bands with artificial neural networks, which is highlighted by the results presented by the confusion matrix (Figure 5). Using methodologies that evaluate the plant phenotype associated with computational intelligence is an accurate and reliable way to measure characteristics when the crop is still in the field [40].  
Our findings demonstrate that it is possible to distinguish soybean genotypes more accurately using spectral bands as input in the tested machine learning models. This represents an important scientific advance for mapping soybean areas in world. For example, in Brazil, a large number of soybean cultivars are used annually, which have several different characteristics from each other, especially regarding the cycle. As in Brazil, soybean is grown in the crop season, being able to distinguish soybean cultivars demonstrates the possibility of introducing public policies for the prevention of end-of-cycle diseases, harvest planning and off-season planting.  
However, it is also necessary that more orbital data be evaluated in the discrimination of plant species, seeking to achieve the absence of clouds, either through data with better spatial resolution (Sentinel-2/MSI) or even via satellite constellations (PlanetScope). Possibly the application of machine learning techniques can bring new results with the different characteristics of the various orbital sensors, even those that are equivalent, as is the case of the new Landsat-9 platform [41].