Classification of Citrus Plant Diseases Using Deep Transfer Learning

Zia Ur Rehman, Muhammad; Ahmed, Fawad; Attique Khan, Muhammad; Tariq, Usman; Shaukat Jamal, Sajjad; Ahmad, Jawad; Hussain, Iqtadar

Abstract

In recent years, the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits. This in turn has helped in improving the quality and production of vegetables and fruits. Citrus fruits are well known for their taste and nutritional values. They are one of the natural and well known sources of vitamin C and planted worldwide. There are several diseases which severely affect the quality and yield of citrus fruits. In this paper, a new deep learning based technique is proposed for citrus disease classification. Two different pre-trained deep learning models have been used in this work. To increase the size of the citrus dataset used in this paper, image augmentation techniques are used. Moreover, to improve the visual quality of images, hybrid contrast stretching has been adopted. In addition, transfer learning is used to retrain the pre-trained models and the feature set is enriched by using feature fusion. The fused feature set is optimized using a meta-heuristic algorithm, the Whale Optimization Algorithm (WOA). The selected features are used for the classification of six different diseases of citrus plants. The proposed technique attains a classification accuracy of 95.7% with superior results when compared with recent techniques.

• Journal Article Type: Article
• Acceptance Date: May 5, 2021
• Online Publication Date: Sep 7, 2021
• Publication Date: 2022
• Deposit Date: Oct 12, 2021
• Publicly Available Date: Oct 12, 2021
• Journal: Computers, Materials & Continua
• Print ISSN: 1546-2218
• Publisher: Tech Science Press
• Peer Reviewed: Peer Reviewed
• Volume: 70
• Issue: 1
• Pages: 1401-1417
• DOI: https://doi.org/10.32604/cmc.2022.019046
• Keywords: Citrus plant; disease classification; deep learning; feature fusion; deep transfer learning
• Public URL: http://researchrepository.napier.ac.uk/Output/2811618