# Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Experimental Area and Treatments Evaluated

#### 2.2. Image Acquisition and Multispectral Models

#### 2.3. Using Machine Learning Models

^{®}CoreTM i5 with 6 Gb RAM.

#### 2.4. Statistical Analysis

## 3. Results

#### 3.1. Spectral Signature of Cultivars

#### 3.2. Correlation between Variables

#### 3.3. Scattering between Variables

#### 3.4. Choosing the Best Model and Best Input

#### 3.5. Confusion Matrix Using ANN’s

## 4. Discussion

#### 4.1. Tested Models

#### 4.2. Tested Inputs

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Location of areas cultivated with soybean cultivars in central pivot systems in southeastern Brazil. Each point represents the repetitions used (pixel by pixel).

**Figure 2.**Average spectral behavior of the five soybean cultivars (

**a**) and their isolated minimum and maximum reflectances (P98y12 RR—cv1 (

**b**), Desafio RR—cv2 (

**c**), M6410 IPRO—cv3 (

**d**), M7110 IPRO—cv4 (

**e**), and NA5909 RR—cv5 (

**f**)).

**Figure 3.**Vegetation indices calculated for the five cultivars (cv) evaluated using monotemporal OLI/Landsat-8 images.

**Figure 4.**Correlogram between different spectral bands and vegetation indices evaluated on five soybean cultivars (P98y12 RR—cv1; Desafio RR—cv2; M6410 IPRO—cv3; M7110 IPRO—cv4; NA5909 RR—cv5).

**Figure 5.**Boxplot for the variables correct classification (%) and Kappa coefficient for discrimination of five soybean cultivars using machine learning (ML) models and different inputs (vegetation indices—VIs, Spectral bands—SBs and SBs + VIs).

**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).

**Table 1.**Vegetation spectral models calculated from spectral bands obtained via Landsat 8 Collection 1 Tier 1 and real-time data TOA reflectance.

Vegetation Indices | Equations |
---|---|

AFRI1600 (Aerosol Free Vegetation Index 1600) | $\left(R{\lambda}_{nir}-0.66\ast \frac{R{\lambda}_{SWIR1}}{R{\lambda}_{nir}+0.66\ast R{\lambda}_{SWIR1}}\right)$ |

ARVI2 (Atmospherically Resistant Vegetation Index 2) | $-0.18+1.17\ast \left(\left(R{\lambda}_{nir}-R{\lambda}_{red}\right)/\left(R{\lambda}_{nir}+R{\lambda}_{red}\right)\right)$ |

ATSAVI (Ajusted Transformed Soil-Ajusted VI) | $1.22\ast \left[\frac{\left(R{\lambda}_{nir}-1.22\ast R{\lambda}_{red}-0.03\right)}{\left(1.22\ast R{\lambda}_{nir}+R{\lambda}_{red}-1.22\ast 0.03+0.08(1+{1.22}^{2}\right)}\right]$ |

EVI (Enhanced Vegetation Index) | $2.5\ast \left(\frac{R{\lambda}_{nir}-R{\lambda}_{red}}{\left(R{\lambda}_{nir}+6\ast R{\lambda}_{red}-7.5\ast R{\lambda}_{blue}\right)+1}\right)$ |

EVI2 (Enhanced Vegetation Index 2) | $2.5\ast \left(R{\lambda}_{nir}-R{\lambda}_{red}\right)/\left(R{\lambda}_{nir}+2.4\ast R{\lambda}_{red}+1\right)$ |

GNDVI (Green Normalized Difference Vegetation Index) | $\frac{\left(R{\lambda}_{nir}-R{\lambda}_{red}\right)}{\left(R{\lambda}_{nir}+R{\lambda}_{red}\right)}$ |

GRNDVI (Green-Red NDVI) | $[R{\lambda}_{nir}-\left(R{\lambda}_{green}+R{\lambda}_{red}\right)]/[R{\lambda}_{nir}+\left(R{\lambda}_{green}+R{\lambda}_{red}\right)$ |

GVI (Tasselled Capvegetation) | $-0.2848\ast R{\lambda}_{blue}-0.2435\ast R{\lambda}_{green}-0.5436\ast R{\lambda}_{red}+0.7243\ast R{\lambda}_{nir}+0.0840\ast R{\lambda}_{SWIR1}-0.1800\ast R{\lambda}_{SWIR2}$ |

GVMI (Global Vegetation Moisture Index) | $\frac{\left(R{\lambda}_{nir}+0.1\right)-\left(R{\lambda}_{SWIR2}+0.02\right)}{\left(R{\lambda}_{nir}+0.1\right)-\left(R{\lambda}_{SWIR2}+0.02\right)}$ |

MNDVI (Modified Normalized Difference Vegetation Index) | $\frac{\left(R{\lambda}_{nir}-R{\lambda}_{SWIR2}\right)}{\left(R{\lambda}_{nir}+R{\lambda}_{SWIR2}\right)}$ |

NDVI (Normalized Difference Vegetation Index) | $\frac{\left(R{\lambda}_{nir}-R{\lambda}_{red}\right)}{\left(R{\lambda}_{nir}+R{\lambda}_{red}\right)}$ |

SBI (Tasselled Cap—brightness) | $0.3037\ast R{\lambda}_{blue}+0.2793\ast R{\lambda}_{green}+0.4743\ast R{\lambda}_{red}+0.5585\ast R{\lambda}_{nir}+0.5082\ast R{\lambda}_{Cirrus}+0.1863\ast R{\lambda}_{SWIR2}$ |

SIWSI (Normalized Difference 860/1640) | $\frac{\left(R{\lambda}_{nir}-R{\lambda}_{SWIR1}\right)}{\left(R{\lambda}_{nir}+R{\lambda}_{SWIR1}\right)}$ |

**Table 2.**Unfolding of the significant model x input interaction for the correct classification (%) of five soybean cultivars using machine learning (ML) models and different inputs (vegetation indices—VIs, spectral bands—SBs, and SBs + VIs).

Model | SBs * | VIs | SBs + VIs |
---|---|---|---|

ANN | 92.18 Aa | 88.30 Ba | 91.12 Aa |

DT | 85.88 Ac | 72.24 Bc | 85.72 Ac |

RBF | 80.94 Ab | 49.50 Be | 74.88 Af |

REPTree | 82.92 Ad | 68.32 Bd | 82.46 Ad |

RF | 89.62 Ae | 80.22 Bb | 87.94 Ab |

SVM | 73.82 Bf | 78.86 Ab | 78.24 Ae |

**Table 3.**Unfolding of the significant model × input interaction for the Kappa coefficient for discrimination of five soybean cultivars using machine learning (ML) models and different inputs (vegetation indices—VIs, spectral bands—SBs, and SBs + VIs).

Model | SBs * | VIs | SBs + VIs |
---|---|---|---|

ANN | 0.91 Aa | 0.86 Ba | 0.89 Aa |

DT | 0.82 Ac | 0.66 Bc | 0.82 Ac |

RBF | 0.76 Ae | 0.37 Ce | 0.68 Bf |

REPTree | 0.79 Ad | 0.60 Bd | 0.78 Ad |

RF | 0.87 Ab | 0.75 Bb | 0.85 Ab |

SVM | 0.67 Bf | 0.73 Ab | 0.74 Ae |

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## Share and Cite

**MDPI and ACS Style**

Gava, R.; Santana, D.C.; Cotrim, M.F.; Rossi, F.S.; Teodoro, L.P.R.; da Silva Junior, C.A.; Teodoro, P.E.
Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models. *Sustainability* **2022**, *14*, 7125.
https://doi.org/10.3390/su14127125

**AMA Style**

Gava R, Santana DC, Cotrim MF, Rossi FS, Teodoro LPR, da Silva Junior CA, Teodoro PE.
Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models. *Sustainability*. 2022; 14(12):7125.
https://doi.org/10.3390/su14127125

**Chicago/Turabian Style**

Gava, Ricardo, Dthenifer Cordeiro Santana, Mayara Favero Cotrim, Fernando Saragosa Rossi, Larissa Pereira Ribeiro Teodoro, Carlos Antonio da Silva Junior, and Paulo Eduardo Teodoro.
2022. "Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models" *Sustainability* 14, no. 12: 7125.
https://doi.org/10.3390/su14127125