Ensemble Machine Learning Approaches for Robust Classification of Maize Plant Leaf Diseases

Abstract

Maize, a fundamental crop globally, is particularly susceptible to a range of leaf diseases, which can result in substantial yield reductions and economic challenges for agricultural producers. Prompt and precise identification of these diseases is critical to minimizing their adverse effects on food security. This study investigates the application of ensemble machine learning methodologies to improve the robustness and accuracy of maize leaf disease classification. For this proposed experiment, the standard dataset has been utilized, dataset contains 3857 images belonging to blight, Common rust, gray leaf spot, and healthy leafs. By using this dataset three kinds of features (Gray-level co-occurrence matrix (GLCM), Local Binary Pattern (LBP) and Gabor) were extracted. This proposed experiment was carried out in three categories i.e., Single, Double and Multiple combination of features. These extracted features are submitted to three machine learning algorithms, such as s (SVM), kNN, and NN. In single feature Gabor with NN Classifier has given 85.40% as highest accuracy, in the Bi-features Gabor with LBP using NN algorithm has record the 88.00% as an output result, at last in Tri-features (Gabor + GLCM+LB) SVM has raised as a highest recognition accuracy as 88.80%.