Identification and Classification of Mango Leaf Disease Using Wavelet Transform based Segmentation and Wavelet Neural Network Model

Abstract

Automatic detection of plant diseases is very ample beneficial as it reduces the workload of farmers. To improve the quality and quantity of crop yield, identification of plant diseases is important in agricultural field. Leaves are considered as the food source for plants and the early and accurate recognition of leaf diseases is very much essential. This research work presents an wavelet transform image segmentation technique for automatic detection  and a WNN(Wavelet neural network)  based approach  presented that classifies leaf diseases in Mango plant species. It is proposed to identify and detect the disease from the mango leaf by taking high resolution images.  The plant Village dataset which is consisting of 1130 images of diseased and healthy mango leaves is considered for segmentation and classification. The proposed WNN model achieves an accuracy of 98.93% for identifying the leaf diseases in mango leaves thereby showing the feasibility of its usage in real time applications.