Clasificación de híbridos de maíz utilizando detección remota multiespectral basada en UAV y algoritmos de aprendizaje automático

Novel methodologies for phenotypic evaluation in maize have been developed through the integration of advanced sensing technologies and machine learning algorithms. The aim of this study was to identify the most accurate machine learning algorithm for the classification of maize hybrids and to determine the optimal input data to enhance model performance. Seven maize hybrids were used in the experiment. After 60 d of crop emergence, the remotely piloted aircraft SenseFly® eBee RTK was used to obtain reflectance values at the following spectral bands (SB): blue (475 nm, B_475), green (550 nm, G_550), red (660 nm, R_660), red edge (735 nm, RE_735) and near-infrared (790 nm, NIR_790). Following the acquisition of spectral band (SB) data, vegetation indices (VIs) were calculated. The resulting dataset was subsequently analyzed using machine learning techniques, evaluating six algorithms: artificial neural networks (ANN), J48 decision trees (J48), REPTree (DT), random forest (RF), support vector machine (SVM) and logistic regression (LR) as the baseline model. Three accuracy metrics were used to evaluate the performance of the algorithms in classifying maize hybrids: correct classifications (CC), Kappa coefficient, and F-score. Among the algorithms tested, ANN showed the highest performance in all three metrics, proving its superiority and potential for real-world applications. Although all three input configurations enhanced classification accuracy for ANN algorithm, the optimal approach is to use only SB as input due to reduced data processing time and increased simplicity.