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Review | Green Intelligent Systems and Applications | Volume 4 - Issue 1 - 2024 | 1-10 | https://doi.org/10.53623/gisa.v4i1.352
© 2024 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Durian Species Classification Using Deep Learning Method

Author(s): Boon Chen Teo , Huong Yong Ting , Abdulwahab Funsho Atanda
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Design and Technology Centre, University of Technology Sarawak, No. 1, Jalan Universiti, 96000 Sibu, Sarawak, Malaysia

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Green Intelligent Systems and Applications

Volume 4 - Issue 1 - 2024

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Durian is a popular fruit in Southeast Asia, and the market offers various species of durians. Accurate species classification is crucial for quality control, grading, and marketing. However, the complexity of this task has led to the utilization of machine learning and deep learning methods. Traditional machine learning algorithms, such as K-Nearest Neighbors, Linear Discriminant Analysis, Support Vector Machines, and Random Forests, have demonstrated good accuracy, but they require extensive feature engineering. Deep learning algorithms, particularly Convolutional Neural Networks, can automatically extract features, making them less dependent on manual feature selection. This research aims to review deep learning classification algorithms, including Convolutional Neural Networks and Recurrent Neural Networks, to determine the most suitable algorithm for an efficient and accurate durian classification system. The objective is to enhance the precision and speed of durian species classification, presenting potential advantages for both durian producers and consumers. The literature review revealed that Convolutional Neural Networks outperformed other deep learning and traditional machine learning algorithms on datasets of varying sizes, achieving the highest accuracy of 98.96% through techniques like image resizing, color conversion, and additional parameters such as days harvested and dry weight. Deep learning emerges as a promising approach for robust and accurate durian species recognition, with future directions including developing models to classify durian species from different plant parts and even real-time video analysis. However, while Convolutional Neural Networks lead the way, a critical research gap exists in identifying optimal features, necessitating further investigation to refine durian species recognition accuracy.

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About this article

SUBMITTED: 09 November 2023
ACCEPTED: 02 January 2024
PUBLISHED: 18 January 2024
SUBMITTED to ACCEPTED: 54 days
DOI: https://doi.org/10.53623/gisa.v4i1.352

Cite this article
Teo, B. C., Ting, H. Y., & Atanda, A. F. . (2024). Durian Species Classification Using Deep Learning Method. Green Intelligent Systems and Applications, 4(1), 1–10. https://doi.org/10.53623/gisa.v4i1.352
Keywords
Durian Image Classification Artificial Intelligence
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