Open Access

ARTICLE

Deep Transfer Learning Based Detection and Classification of Citrus Plant Diseases

Shah Faisal¹, Kashif Javed¹, Sara Ali¹, Areej Alasiry², Mehrez Marzougui², Muhammad Attique Khan^3,*, Jae-Hyuk Cha^4,*

1 Department of Robotics and Artificial Intelligence, SMME NUST, Islamabad, Pakistan
2 College of Computer Science, King Khalid University, Abha, 61413, Saudi Arabia
3 Department of Computer Science, HITEC University, Taxila, Pakistan
4 Department of Computer Science, Hanyang University, Seoul, 04763, Korea

* Corresponding Authors: Muhammad Attique Khan. Email: ; Jae-Hyuk Cha. Email:

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

Computers, Materials & Continua 2023, 76(1), 895-914. https://doi.org/10.32604/cmc.2023.039781

Received 15 February 2023; Accepted 14 April 2023; Issue published 08 June 2023

Abstract

Citrus fruit crops are among the world’s most important agricultural products, but pests and diseases impact their cultivation, resulting in yield and quality losses. Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade, allowing for early disease detection and improving agricultural production. This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning (DL) model, which improved accuracy while decreasing computational complexity. The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy. Using transfer learning, this study successfully proposed a Convolutional Neural Network (CNN)-based pre-trained model (EfficientNetB3, ResNet50, MobiNetV2, and InceptionV3) for the identification and categorization of citrus plant diseases. To evaluate the architecture’s performance, this study discovered that transferring an EfficientNetb3 model resulted in the highest training, validating, and testing accuracies, which were 99.43%, 99.48%, and 99.58%, respectively. In identifying and categorizing citrus plant diseases, the proposed CNN model outperforms other cutting-edge CNN model architectures developed previously in the literature.

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Keywords

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