Smartphone assist deep neural network to detect the citrus diseases in Agri-informatics
Utpal Barman, Ridip Dev Choudhury
Abstract
The citrus family provides healthy fruits to humans. The quality and quantity of the citrus fruits depend on the quality of the citrus leaves. Due to the diseases of citrus leaves, the quality and productivity of citrus fruit are degraded. This paper provides a computer automation system to detect the diseases of the citrus leaves using machine learning and deep learning techniques. In this paper, images of citrus leaves are captured using an Android Smartphone in natural environmental light. Three different classes of citrus leaves are collected using the Smartphone and destructive method. These are citrus healthy, citrus greening, and citrus CTV (Citrus Tristeza virus). Citrus images are processed for image resizing, image noise removal, image enhancement, and feature extraction. Features of the images are calculated using Gray Level Co-occurrence Matrix in the color and gray domain of the images. Finally, the detection and classification are done using K- Nearest Neighbor (KNN) and Deep Neural Network (DNN) classifier. The accuracy of the KNN classifier is 89.9% for the K value 3 whereas the accuracy of DNN is 99.89% with an error of 0.0219. The proposed system helps the farmer to detect diseases at an early stage.