AccScience Publishing / IJAMD / Online First / DOI: 10.36922/IJAMD025150011
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ORIGINAL RESEARCH ARTICLE

Automated fruit sorting system integrating image processing and support vector machine techniques

Babatunde Olayinka Oyefeso1 ,  Oluwaseun Emmanuel Oyewande1 ,  John Audu2*
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1 Department of Agricultural and Environmental Engineering, Faculty of Technology, University of Ibadan, Ibadan, Oyo State, Nigeria
2 Department of Agricultural and Bio-systems Engineering, College of Engineering, Joseph Sarwuan Tarka, University Makurdi, Benue State, Nigeria
IJAMD, 025150011 https://doi.org/10.36922/IJAMD025150011
Received: 8 April 2025 | Revised: 18 May 2025 | Accepted: 22 May 2025 | Published online: 20 June 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Traditional fruit grading methods are mostly time-consuming and subjective, thereby limiting efficiency in the agricultural sector. To address these problems, this paper presents the design and implementation of an automated fruit sorting system for classifying certain fruits, namely oranges, tomatoes, and mangoes, using image processing and support vector machine (SVM) techniques. An ESP32 camera was used to capture images of the fruits, which were later passed through algorithms in Python. Extracted features were then fed into a SVM model for the classification process of fruits. The model demonstrated excellent performance, achieving an accuracy of 100%, a precision of 96%, a recall of 92%, and an F1 score of 89%. The results indicated that incorporating multiple features significantly increases the accuracy of the classification. Moreover, the performance was optimized by selecting an appropriate regularization parameter during the training of the model and the use of polynomial kernel functions. Finally, the whole automated system was assembled to physically sort the classified fruits into different containers. This research highlights the potential of integrating image processing and machine learning technologies to revolutionize fruit classification processes, thereby improving both efficiency and quality control in agriculture.

Graphical abstract
Keywords
Image processing
Fruit classification
Support vector machine
Automated sorting
Feature extraction
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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