High-value fruit biometric identification via triplet-loss technique

Name: Plaifah Laimek

keyword: Agriculture

; Authentication

; Fruits

; Melon

; Rind pattern

; Identification

; Verification

; Computer vision

; Machine learning

Abstract: This thesis proposes a novel method for biometric authentication of fruits based on their distinctive rind patterns, similar to fingerprint identification. Luxury fruits, highly valued in Japan, currently rely on serial numbers, QR codes, and RFID tags for authentication, which can be forged or replicated. By implementing biometric authentication using rind patterns, the trust and value of these fruits can be significantly enhanced, while also preventing fraud and counterfeiting in the agricultural industry. The study introduces a melon identification system that utilizes a convolutional neural network (CNN) with a triplet loss function, enabling accurate identification even with variations in lighting, shadows, and angle. The proposed method overcomes the limitations of previous approaches by capturing important features through CNN’s Automatic feature identification. This research contributes to the field of agricultural product authentication, providing a secure and reliable method that can be extended to other products, increasing customer trust and market value

Thammasat University. Thammasat University Library

Address: BANGKOK

Email: preserv@tu.ac.th

Name: Waree Kongprawechnon

Role: advisor

Created: 2022

Modified: 2024-01-02

Issued: 2024-01-02

วิทยานิพนธ์/Thesis

application/pdf

DOI: 10.14457/TU.the.2022.766

eng

DegreeName: Master of Engineering

Level: Master's Degree

Descipline: Artificial Intelligence and Internet of Things

Grantor: Thammasat University

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