Estimate of soybean crop productivity in the 2021/22 season: Vegetation indices and Machine Learning
DOI:
https://doi.org/10.14295/bjs.v2i1.247Keywords:
Glycine max, random forest, Google Colaboratory, remote sensingAbstract
Soybean is one of the most economically important crops in the world, with Brazil being the world's largest producer of this grain. Knowing the productivity is not always possible since these are linked to the type of technology that the farm has and allows an indirect evaluation of the quality of management. Thus, the objective was to estimate the productivity of the soybean crop in the 21/22 season in southwest Goiás using vegetation indices and machine learning. The vegetation indices EVI, NDRE, NDVI, NDWI and the reflectance values of the RGB composition of the Sentinel 2A and 2B satellite were used, harmonized images, free of clouds with one before sowing, during plowing and one image after harvesting. Random points were obtained for each of the six productivity classes and vegetation index values were assigned for each date and class. The data matrix was processed on the Google Collaboratory platform using the Random Forest classifier from the Scikit-learn package. Evaluating all parameters allowed by Random Forest, the best score (0.6825) to estimate soybean productivity was obtained using the gini criteria, 85% of samples and 120 estimators, using all recurrent images of the harvest period 21/22 and images before sowing and after harvesting.
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