首页|University of Sevilla Researchers Advance Knowledge in Machine Learning (Tacklin g unbalanced datasets for yellow and brown rust detection in wheat)
University of Sevilla Researchers Advance Knowledge in Machine Learning (Tacklin g unbalanced datasets for yellow and brown rust detection in wheat)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news reporting from Seville, Spai n, by NewsRx journalists, research stated, "This study evaluates the efficacy of hyperspectral data for detecting yellow and brown rust in wheat, employing mach ine learning models and the SMOTE (Synthetic Minority Oversampling Technique) au gmentation technique to tackle unbalanced datasets." Our news editors obtained a quote from the research from University of Sevilla: "Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (R F), and Gaussian Naive Bayes (GNB) models were assessed. Overall, SVM and RF mod els showed higher accuracies, particularly when utilizing SMOTE-enhanced dataset s. The RF model achieved 70% accuracy in detecting yellow rust wit hout data alteration. Conversely, for brown rust, the SVM model outperformed oth ers, reaching 63% accuracy with SMOTE applied to the training set. This study highlights the potential of spectral data and machine learning (ML) techniques in plant disease detection."
University of SevillaSevilleSpainE uropeCyborgsEmerging TechnologiesMachine Learning
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