Machine Learning Applied in NDVI Image for Forescasting Sugarcane Productivity
Keywords:
Machie Learning, Sugar Cane, NDVIAbstract
This article presents a model based on ML (Machine Learning) applied to NDVI Images to estimate productivity in the culture of Sugarcane. The use of human techniques based on cognitive and historical productivity experiences are prevalents. The images used were the NDVI (Normalized Difference Vegetation Index), provided by the Sentinel-2 satellite with a 10m multispectral spatial resolution. The data set was obtained from the georeferencing points of the plots (geographic space of the planting areas), where the images are extracted and processed. Predictive algorithm models were used: (i) CNN (Convolution Neural Network), (ii) KNN (K-Nearest Neighbors), (iii) RF (Random Forest), (iv) SVM (Support Vector Machie), (v) AdaBoost (Adaptive Boosting). The RF algorithm proved to be more efficient, so that the results for the DP (Standard Deviation) the formula for the MSE obtained 30.71 tons (t) and the MAE obtained 3.73 (t). In the empirical estimates, the DP for the MSE obtained 34.71 (t) and the MAE 3.97 (t). The EM (Mean Error) for the empirical estimate was -8.80% and the algorithm was 0.012%. So this work shows consistent results and can be used to estimate productivity in the culture of Sugarcane.
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