An Ensemble Classifier-Based Model Development for Mango Leaf Diseases Using Hybrid Feature Approach

Mango leaves suffer from several bacterial and fungus diseases that affect the growth of tree as well as fruit. The severity of the illness is indicated by its various degrees. The photos of the diseased and healthy leaves were evaluated at various stages to determine the kind of illness for early detection. In this work, a classifier-based method is proposed to classify the extracted features from different diseases like anthracnose, bacterial canker, black sooty mold using an ensemble classifier, it uses a discriminant learning technique for subspace ensemble. Other classifier performance was compared to that of the subspace discriminant ensemble classifier. The simulation results demonstrated that the suggested subspace discriminant ensemble outperformed the other classifiers with 95.1% accuracy.