A Discriminant Ensemble Classifier Model for Hybrid Feature Vector for Efficient Detection of Diseases in Mango Leaves

Ensuring a healthy mango crop and stop infections from spreading, early diagnosis of mango illnesses is essential. Digital techniques for detecting mango diseases involve using technology to identify and monitor diseases in mango crops. These techniques can aid in early disease diagnosis and prompt action by farmers to stop the spread of illness. Digital techniques use various classifier for detecting mango diseases from extracted features of the segmented images like SVM, Naive bayes, Fine tree, neural networks etc. In this work three diseases namely anthracnose, black sooty mold and bacterial canker and healthy leaf images are taken. Images are pre processed by applying different techniques like denoising, edge sharping and pre processed images are colour segmented, from segmented images, feature vector was prepared with various features including colour, texture and geometric feature, which are classified by various available classifiers and results are compared and shows that ensemble classifier gives better results with proposed methodology.