Tackling unbalanced datasets for yellow and brown rust detection in wheat - Cuenca-Romero, Carmen; Apolo-Apolo, Orly Enrique; Rodríguez Vázquez, Jaime Nolasco; Egea, Gregorio; Pérez-Ruiz, Manuel (Frontiers in Plant Science, 2024)
This study assesses the efficacy of hyperspectral data in detecting yellow and brown rust in wheat. Machine learning models—ANN (Artificial Neural Network), SVM (Support Vector Machine), RF (Random Forest), and GNB (Gaussian Naïve Bayes)—were evaluated, with SVM and RF models demonstrating the highest accuracies, mainly when SMOTE (Syntenic Minority Over-Sampling Technique) -enhanced datasets were used. For yellow rust, the RF model achieved 70% accuracy using unaltered data, while for brown rust, the SVM model performed best, with an accuracy of 63% when SMOTE was applied to the training set. The study underscores the potential of spectral data and machine learning (ML) in plant disease detection. It calls for advanced research in data processing techniques, particularly in applying SMOTE and its impact on model performance.