Open Access

ARTICLE

Sailfish Optimizer with EfficientNet Model for Apple Leaf Disease Detection

by Mazen Mushabab Alqahtani¹, Ashit Kumar Dutta², Sultan Almotairi³, M. Ilayaraja⁴, Amani Abdulrahman Albraikan⁵, Fahd N. Al-Wesabi^6,7,*, Mesfer Al Duhayyim⁸

1 Physical Therapy Department, Majmaah University, Majmaah, 11952, Saudi Arabia
2 Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, 13713, Riyadh, Saudi Arabia
3 Department of Natural and Applied Sciences, Faculty of Community College, Majmaah University, Majmaah, 11952, Saudi Arabia
4 School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, 626128, India
5 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia
6 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, 62529, Saudi Arabia
7 Faculty of Computer and IT, Sana’a University, Sana’a, 1247, Yemen
8 Department of Natural and Applied Sciences, College of Community - Aflaj, Prince Sattam Bin Abdulaziz University, Saudi Arabia

* Corresponding Author: Fahd N. Al-Wesabi. Email:

Computers, Materials & Continua 2023, 74(1), 217-233. https://doi.org/10.32604/cmc.2023.025280

Received 18 November 2021; Accepted 17 January 2022; Issue published 22 September 2022

Abstract

Recent developments in digital cameras and electronic gadgets coupled with Machine Learning (ML) and Deep Learning (DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models. In this background, the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection (ESFO-EALD) model. The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically. In this scenario, Median Filtering (MF) approach is utilized to boost the quality of apple plant leaf images. Moreover, SFO with Kapur's entropy-based segmentation technique is also utilized for the identification of the affected plant region from test image. Furthermore, Adam optimizer with EfficientNet-based feature extraction and Spiking Neural Network (SNN)-based classification are employed to detect and classify the apple plant leaf images. A wide range of simulations was conducted to ensure the effective outcomes of ESFO-EALD technique on benchmark dataset. The results reported the supremacy of the proposed ESFO-EALD approach than the existing approaches.

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