Open Access

ARTICLE

Classification of Citrus Plant Diseases Using Deep Transfer Learning

Muhammad Zia Ur Rehman¹, Fawad Ahmed¹, Muhammad Attique Khan², Usman Tariq³, Sajjad Shaukat Jamal⁴, Jawad Ahmad^5,*, Iqtadar Hussain⁶

1 Department of Electrical Engineering, HITEC University, Taxila, Pakistan
2 Department of Computer Science, HITEC University, Taxila, Pakistan
3 College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Khraj, Saudi Arabia
4 Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
5 School of Computing, Edinburgh Napier University, UK
6 Department of Mathematics, Statistics, and Physics, Qatar University, Doha, 2713, Qatar

* Corresponding Author: Jawad Ahmad. Email:

(This article belongs to the Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)

Computers, Materials & Continua 2022, 70(1), 1401-1417. https://doi.org/10.32604/cmc.2022.019046

Received 31 March 2021; Accepted 05 May 2021; Issue published 07 September 2021

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.

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Citations