Convolutional Neural Network Based Deep Learning Model for Accurate Classification of Durian Types

Diana Diana, Tri Basuki Kurniawan, Deshinta Arrova Dewi, Mashal Kasem Alqudah, Musab Kasim Alqudah, Mohd Zaki Zakari, Eyna Fahera Binti Eddie Fuad
Pages 101-114
Abstract Views 1248
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DOI https://doi.org/10.47738/jads.v6i1.480

Abstract


Durian recognition is significant among fans of the durian community since many people tend to get confused, especially if they are not familiar with durian species, which can lead them to be involved in durian fraud. The development of this prototype can detect and classify durian fruits into three categories, including Musang King, Black Thorn, and D24, which can significantly benefit consumers. The prototype in this research involves training using a dataset of durian images, specifically in Musang King, Black Thorn, and D24 varieties. Preprocessing techniques such as resizing and scaling data are applied to enhance the quality and consistency of the dataset. The models chosen to develop this prototype include VGG-16 and Xception, and each model is compared according to its accuracy percentage. The accuracy outcomes of VGG-16 and Xception models are 56.64% and 92%, respectively. The models used a total of 1,372 images of durian with three classifications. Based on the findings, further enhancement of the CNN models for durian classification can be done by implementing different architectures, techniques, and methods. Moreover, future models can consider real-time image capture and processing capabilities to enhance the practicality of the system for durian consumers. The prototype developed in this study demonstrates the feasibility of using deep learning techniques for accurate and efficient durian classification, paving the way for future advancements in automated fruit grading and quality control systems in the durian industry.


Keywords


Durian Recognition; Deep Learning; Image Classification; Product Innovation; Process Innovation

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References


N. A. Husin, S. Rahman, R. Karunakaran, and S. J. Bhore, “A review on the nutritional, medicinal, molecular and genome attributes of Durian (Durio zibethinus L.), the King of fruits in Malaysia,” Bioinformation, vol. 14, no. 06, pp. 265–270, Jun. 2018, doi: 10.6026/97320630014265.

M. Mohd Ali, N. Hashim, S. A. Aziz, and O. Lasekan, “Exploring the chemical composition, emerging applications, potential uses, and health benefits of durian: A review,” Food Control, vol. 113, p. 107189, 2020, doi: https://doi.org/10.1016/j.foodcont.2020.107189.

S. Safari, A. Razali, W. Mahfuzah, W. Ibrahim, M. Sukri, and A. Rahim, “From farm to China: A case study of Malaysian frozen whole durian export supply chain (Dari ladang ke China: Kajian kes rantaian bekalan eksport durian sejuk beku Malaysia dan bentuk biji),” 2021.

S. Ketsa, A. Wisutiamonkul, Y. Palapol, and R. E. Paull, “The Durian,” in Horticultural Reviews, 2020, pp. 125–211. doi: https://doi.org/10.1002/9781119625407.ch4.

N. Mohd Nordin, H. Anuar, Y. F. Buys, F. Ali, S. Thomas, and N. A. Mohd Nasir, “Effect of freeze-dried durian skin nanofiber on the physical properties of poly(lactic acid) biocomposites,” Polymer Composites, vol. 42, no. 2, pp. 842–848, Feb. 2021, doi: https://doi.org/10.1002/pc.25869.

N. A. A Aziz and A. M. Mhd Jalil, “Bioactive Compounds, Nutritional Value, and Potential Health Benefits of Indigenous Durian (Durio Zibethinus Murr.): A Review,” Foods, vol. 8, no. 3, 2019, doi: 10.3390/foods8030096.

S. Thongkaew, C. Jatuporn, P. Sukprasert, P. Rueangrit, and S. Tongchure, “Factors affecting the durian production of farmers in the eastern region of Thailand,” International Journal of Agricultural Extension, vol. 9, no. 2, pp. 285–293, 2021, doi: 10.33687/ijae.009.02.3617.

C. Airriess, “Constructing durian terroir and geographical indications in Penang, Malaysia,” Singapore Journal of Tropical Geography, vol. 41, no. 1, pp. 6–22, Jan. 2020, doi: https://doi.org/10.1111/sjtg.12298.

C. S. LENG, N. M. I. M. NOR, L. ZAKARIA, and S. N. M. SIDIQUE, “DURIAN STEM CANKER STATUS AND RELATIVE SOIL PROPERTIES IN PENINSULAR MALAYSIA,” Journal of Sustainability Science and Management, vol. 19, no. 6, pp. 85–97, Jun. 2024, doi: 10.46754/jssm.2024.06.007.

L. T. Duong, P. T. Nguyen, C. di Sipio, and D. di Ruscio, “Automated fruit recognition using EfficientNet and MixNet,” Computers and Electronics in Agriculture, vol. 171, p. 105326, 2020, doi: https://doi.org/10.1016/j.compag.2020.105326.

O. Elijah, T. A. Rahman, I. Orikumhi, C. Y. Leow, and M. N. Hindia, “An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges,” IEEE Internet of Things Journal, vol. 5, no. 5, pp. 3758–3773, 2018, doi: 10.1109/JIOT.2018.2844296.

A. S. Prabowo and L. J. Mawarani, “Edible Coating Development of Durian Seeds Starch and Glucomannan with the Addition of Essential Oil As An Antimicrobial to Increase Shelf Life of Tomato and Cauliflower,” IOP Conference Series: Materials Science and Engineering, vol. 833, no. 1, Jun. 2020, doi: 10.1088/1757-899X/833/1/012034.

X. Lin et al., “Assessment of Genetic Diversity and Discovery of Molecular Markers in Durian (Durio zibethinus L.) in China,” Diversity, vol. 14, no. 9, 2022, doi: 10.3390/d14090769.

S. Wan and S. Goudos, “Faster R-CNN for multi-class fruit detection using a robotic vision system,” Computer Networks, vol. 168, p. 107036, 2020, doi: https://doi.org/10.1016/j.comnet.2019.107036.

M. G. Lim and J. H. Chuah, “Durian Types Recognition Using Deep Learning Techniques,” in 2018 9th IEEE Control and System Graduate Research Colloquium (ICSGRC), 2018, pp. 183–187. doi: 10.1109/ICSGRC.2018.8657535.

D. N. V. S. L. S. Indira, J. Goddu, B. Indraja, V. M. L. Challa, and B. Manasa, “A review on fruit recognition and feature evaluation using CNN,” Materials Today: Proceedings, vol. 80, pp. 3438–3443, 2023, doi: https://doi.org/10.1016/j.matpr.2021.07.267.

Y. A. S. A. F. A. Harmandeep Singh Gill Osamah Ibrahim Khalaf, “Multi-Model CNN-RNN-LSTM Based Fruit Recognition and Classification,” Intelligent Automation & Soft Computing, vol. 33, no. 1, pp. 637–650, 2022, doi: 10.32604/iasc.2022.022589.

S. S. S. Palakodati, V. R. Chirra, Y. Dasari & S. Bulla. Fresh and Rotten Fruits Classification Using CNN and Transfer Learning. Revue D’Intelligence Artificielle, 34(5), 617–622, 2020, https://doi.org/10.18280/ria.340512.

X. Yang, Z. Zeng, S. G. Teo, L. Wang, V. Chandrasekhar, and S. Hoi, “Deep Learning for Practical Image Recognition: Case Study on Kaggle Competitions,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, in KDD ’18. New York, NY, USA: Association for Computing Machinery, 2018, pp. 923–931. doi: 10.1145/3219819.3219907.

EDEN BARUA. (2021). Durian Dataset. https://www.kaggle.com/datasets/edenbarua/picture/data

Sujang, G. B., Ramaiya, S. D., Saupi, N., & Lee, S. Y. (2023). Profiling of Volatile Organic Compounds (VOCs) of Wild Edible Durians from Sarawak, Borneo Associated with Its Aroma Properties. Horticulturae, 9(2), 257.




DOI: https://doi.org/10.47738/jads.v6i1.480

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Journal of Applied Data Sciences

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