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

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.

Keywords

Image processing / Fruit classification / Support vector machine / Automated sorting / Feature extraction

Acknowledgments

None.

Funding

None.

Conflict of interest

The authors declare that they have no competing interests.

Author contributions

Conceptualization: All authors

Data curation: Oluwaseun Emmanuel Oyewande, John Audu

Formal analysis: All authors

Investigation: Oluwaseun Emmanuel Oyewande

Methodology: All authors

Project administration: Babatunde Olayinka Oyefeso, John Audu

Resources: All authors

Software: Oluwaseun Emmanuel Oyewande, John Audu

Supervision: Babatunde Olayinka Oyefeso, John Audu

Validation: Babatunde Olayinka Oyefeso, John Audu

Visualization: Oluwaseun Emmanuel Oyewande, John Audu

Writing - original draft: Oluwaseun Emmanuel Oyewande

Writing - review & editing: John Audu

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data

The code, analysis scripts, and datasets supporting this article have been included as part of the supplementary material.

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