Recognition of Jute Diseases by Leaf Image Classification using Convolutional Neural Network

As Convolutional Neural Network (CNN) is achieving the state-of-the-art in the field of image classification, this research work focuses on the finding prominent accuracy of the jute leaf image diseases using deep learning approach. Acquiring the better performance in disease identification is the main purpose of this paper. Among different types of jute leaf diseases, Chlorosis and Yellow Mosaic have been selected to recognize the diseased leaves from the healthy leaves. As per our knowledge, no other method for leaf disease detection of Jute plant has been proposed for the first time. Using a dataset of 600 images, proposed model is aimed to classify two common jute leaf diseases. CNN achieves an overall accuracy of 96% without applying any image preprocessing and feature extraction method. The results suggest that proposed deep learning model provides an improved solution in disease control for jute leaf diseases with high accuracy.