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

• Published in: Journal of Applied Data Sciences
• Main Author: Diana D.; Kurniawan T.B.; Dewi D.A.; Alqudah M.K.; Alqudah M.K.; Zakari M.Z.; Fuad E.F.B.E.
• Format: Article
• Language: English
• Published: Bright Publisher 2025
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216751803&doi=10.47738%2fjads.v6i1.480&partnerID=40&md5=1adea91f846cbcb1703ba2092d41391b

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. © Authors retain all.