 |
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
|
Issue: November 2025
Volume 10 | Issue 11
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 114
Article Submitted : 18702
Article Published : 7876
Total Authors : 20810
Total Reviewer : 757
Total Countries : 142
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | Feature Based Fruit Classification using Machine Learning Algorithms: A Comparison |
| Authors Name: | Zeeshan Ali Haq , Zainul Abdin Jaffery , Shabana Mehfuz |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_201108
|
| Published Paper Id: | IJRTI2503059 |
| Published In: | Volume 10 Issue 3, March-2025 |
| DOI: | http://doi.one/10.1729/Journal.44137 |
| Abstract: | Classification of fruits into different categories on the basis of their species, quality, size or shape is an aspect which the research community are trying to automate for over a decade. Due to limitations of manual process of segregation of fruits into their required respective class, the development of such smart systems is highly required. In this paper, based color and edge feature, three species of fruits including apples, bananas, and oranges are segregated into their respective classes using machine learning algorithms. For these three types of fruits, for the dataset development, total of 9600 images were acquired. To evaluate the performance of the machine learning based algorithms, five parameters including accuracy, Jaccard score, precision, recall, and F-1 Score are evaluated and compared to determine the best suitable algorithm for classification of fruits. Four machine learning algorithms and the traditional convolutional neural network (CNN) based classification models were used for classification of fruits into their respective classes. From the results it was observed that Decision Tree and Random Forest based models were best suited for classification of fruits with a high accuracy of 98.47% and 98.63% respectively. |
| Keywords: | Smart Agriculture, Fruit Classification, Machine Learning, Image Processing, Human Computer Interaction |
| Cite Article: | "Feature Based Fruit Classification using Machine Learning Algorithms: A Comparison ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 3, page no.a452-a459, March-2025, Available :http://www.ijrti.org/papers/IJRTI2503059.pdf |
| Downloads: | 000401 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2503059
Registration ID:201108
Published In: Volume 10 Issue 3, March-2025
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.44137
Page No: a452-a459 Country: New Delhi, Delhi, India Research Area: Electrical Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2503059 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2503059 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2025 - All Rights Reserved - IJRTI | |||