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Fruit Image Classification using Optimal Features Extracted by Soft-Computing Techniques

by Harmandeep Singh Gill
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 103
Year of Publication: 2026
Authors: Harmandeep Singh Gill
10.5120/ijca63a9c28e2a15

Harmandeep Singh Gill . Fruit Image Classification using Optimal Features Extracted by Soft-Computing Techniques. International Journal of Computer Applications. 187, 103 ( May 2026), 15-25. DOI=10.5120/ijca63a9c28e2a15

@article{ 10.5120/ijca63a9c28e2a15,
author = { Harmandeep Singh Gill },
title = { Fruit Image Classification using Optimal Features Extracted by Soft-Computing Techniques },
journal = { International Journal of Computer Applications },
issue_date = { May 2026 },
volume = { 187 },
number = { 103 },
month = { May },
year = { 2026 },
issn = { 0975-8887 },
pages = { 15-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number103/fruit-image-classification-using-optimal-features-extracted-by-soft-computing-techniques/ },
doi = { 10.5120/ijca63a9c28e2a15 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-05-17T02:29:11.138384+05:30
%A Harmandeep Singh Gill
%T Fruit Image Classification using Optimal Features Extracted by Soft-Computing Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 103
%P 15-25
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fruit classification is found to be one of the emerging area in computer vision. The accuracy of the classification system depends on the quality of acquired images, number of features, types of features, selection of features from extracted features and type of classifier used. Images taken in poor environment conditions decrease the visibility and hidden information of digital images. Therefore, image enhancement techniques are necessary for improving the significant details of these images. This study provides a review to highlight recent progress of image enhancement techniques for improving the visibility of fruit images. In this paper, a new fuzzy type-II based image enhancement technique is designed to improve the quality of visibly weather degraded fruit images. The type-II fuzzy logic can automatically extract the local atmospheric light and roughly eliminate the atmospheric veil in local detail enhancement. Thereafter, Teacher-learning based optimization (TLBO) algorithm is used to find an optimal combination of threshold values at different levels for minimizing the cross entropy. TLBO algorithm is inspired by passing on knowledge within a classroom environment where students _rst gain knowledge from a teacher and then through mutual interaction. From experimental results, it is observed that this method is an efficient and feasible method to search an optimal combination of threshold values. Afterwards, fruit images will be classified using Convolution neural network (CNN) and Recurrent neural network (RNN). CNN is utilized to develop discriminative characteristics and RNN is utilized to develop sequential labels. Extensive experiments have been carried out by considering the proposed techniques (i.e., CNN, RNN without type-II fuzzy logic and CNNRNN with type-II fuzzy logic) and existing competitive techniques on fruit images. It is observed that the proposed technique outperforms existing image classification techniques in terms of accuracy and coefficient of correlation. Another contribution of this study is proposal of new image classification technique for weather degraded fruit images. The proposed method is tested on number of fruit images and observed that proposed technique outperforms existing image classification techniques in terms of accuracy. Results of the study are quite promising and justify significance of proposed research.

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Index Terms

Computer Science
Information Sciences

Keywords

CNN RNN LSTM Deep Learning

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