Automated grading of citrus suhuiensis fruit using deep learning method

Mohmad, Faris Azizi and Mohd Khairuddin, Anis Salwa and Mohamed Shah, Noraisyah (2022) Automated grading of citrus suhuiensis fruit using deep learning method. Lecture Notes in Electrical Engineering, 834. 95 – 101. ISSN 1876-1100, DOI

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An automated grading system is important in assisting the farmers to perform quality inspection in a more effective manner as compared to manual approach. Besides that systematic fruit grading is a requirement for effective fruit and vegetable marketing. This is because delivering immature, and bruised fruits will lead to lower market price. Hence, this work proposed an automated Citrus suhuiensis fruit grading system based on image processing that can detect multi-index simultaneously such as maturity, quality and size of a local fruit. The fruits are classified according to the grading specification provided by Federal Agricultural Marketing Authority (FAMA). A convolutional neural network method is adopted to perform the classification process. A total of 303 training images and 75 test images were used in maturity dataset, whilst total of 283 training images and 68 test images were used in quality dataset. Experimental results showed that the proposed classification model able to classify the fruits into 6 classes of maturity and 3 classes of quality. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Item Type: Article
Funders: Universiti Malaya, GPF042A-2019
Additional Information: Cited by: 1; Conference name: International Conference on Computational Intelligence in Machine Learning, ICCIML 2021; Conference date: 1 June 2021 through 2 June 2021; Conference code: 274369
Uncontrolled Keywords: Automation; Citrus fruits; Classification (of information); Convolution; Convolutional neural networks; Deep learning; Grading; Marketing; Statistical tests; Automated grading; Automated grading systems; Convolutional neural network; Fruit and vegetables; Fruit grading; Learning methods; Low markets; Quality inspection; Test images; Training image; Commerce
Subjects: S Agriculture > S Agriculture (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering > Department of Electrical Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 19 Nov 2023 13:10
Last Modified: 19 Nov 2023 13:10

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