Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network

Abstract

Abstract: Citrus fruit diseases are the major cause of extreme citrus fruit yield declines. Plant disease detection and classification are crucial long term agriculture. Manually monitoring citrus diseases is quite tough. As a result, image processing is used for designing an automated detection system for citrus plant diseases. Image acquisition, image preprocessing, image segmentation, feature extraction and classification are main processes in the citrus disease detection process. Deep learning methods have recently obtained promising results in a number of artificial intelligence issues, leading us to apply them to the challenge of recognizing citrus fruit and leaf diseases. In this approach, an integrated approach is used to suggest a convolutional neural networks (CNNs) model. The proposed CNN model is intended to differentiate healthy fruits and leaves from fruits/leaves with common citrus diseases such as black spot, canker and citrus blight. The proposed CNN model extracts complementary discriminative features by integrating multiple layers.