Classifiers based on artificial intelligence in the prediction of recently planted coffee cultivars using a Remotely Piloted Aircraft System

Abstract

The classification and prediction methods through artificial intelligence algorithms are applied in different sectors to assist and promote intelligent decision-making. In this sense, due to the great importance in the cultivation, consumption and export of coffee in Brazil and the technological application of the Remotely Piloted Aircraft System (RPAS) this study aimed to compare and select models based on different data classification techniques by different classification algorithms for the prediction of different coffee cultivars (Coffea arabica L.) recently planted. The attributes evaluated were height, crown diameter, total chlorophyll content, chlorophyll A and chlorophyll B, Foliar Area Index (LAI) and vegetation indexes NDVI, NDRE, MCARI1, GVI, and CI in six months. The data were prepared programming language Python using algorithms of Decision Trees, Random Forest, Support Vector Machine and Neural Networks. It was evaluated through cross-validation in all methods, the distribution by FreeViz, the hit rate, sensitivity, specificity, F1 score, and area under the ROC curve and percentage and predictive performance difference. All algorithms showed good hits and predictions for coffee cultivars (0.768% Decision Tree, 0.836% Random Forest, 0.886 Support Vector Machine and 0.899 Neural Networks) and the Neural Networks algorithm produced more accurate predictions than other tested algorithm models, with a higher percentage of hits for the classes considered.