Open Access

ARTICLE

Plant Identification Using Fitness-Based Position Update in Whale Optimization Algorithm

Ayman Altameem¹, Sandeep Kumar², Ramesh Chandra Poonia³, Abdul Khader Jilani Saudagar^4,*
1 Department of Computer Science, College of Applied Studies, King Saud University, Riyadh, 11495, Saudi Arabia
2 Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India
3 Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India
4 Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
* Corresponding Author: Abdul Khader Jilani Saudagar. Email:
(This article belongs to this Special Issue: Recent advancements in Environment Sustainability, AgriFood using applied artificial intelligence in Multimedia Systems)

Computers, Materials & Continua 2022, 71(3), 4719-4736. https://doi.org/10.32604/cmc.2022.022177

Received 30 July 2021; Accepted 22 September 2021; Issue published 14 January 2022

Abstract

Since the beginning of time, humans have relied on plants for food, energy, and medicine. Plants are recognized by leaf, flower, or fruit and linked to their suitable cluster. Classification methods are used to extract and select traits that are helpful in identifying a plant. In plant leaf image categorization, each plant is assigned a label according to its classification. The purpose of classifying plant leaf images is to enable farmers to recognize plants, leading to the management of plants in several aspects. This study aims to present a modified whale optimization algorithm and categorizes plant leaf images into classes. This modified algorithm works on different sets of plant leaves. The proposed algorithm examines several benchmark functions with adequate performance. On ten plant leaf images, this classification method was validated. The proposed model calculates precision, recall, F-measurement, and accuracy for ten different plant leaf image datasets and compares these parameters with other existing algorithms. Based on experimental data, it is observed that the accuracy of the proposed method outperforms the accuracy of different algorithms under consideration and improves accuracy by 5%.

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Keywords

Bag-of-features; feature optimization; plant leaf classification; swarm intelligence; nature-inspired algorithm

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