Deep learning to detect and classify the purity level of Luwak coffee green beans

Hendrawan Yusuf, . and Widyaningtyas Shinta, . and Fauzy Muchammad Riza, . and Sucipto Sucipto, . and Damayanti Retno, . and Al Riza Dimas Firmanda, . and Hermanto Mochamad Bagus, . and Sandra Sandra, . Deep learning to detect and classify the purity level of Luwak coffee green beans. pp. 1-18. ISSN 2231-8526

Luwak coffee (palm civet coffee) is known as one of the most expensive coffee in the world. In order to lower production costs Indonesian producers and retailers often mix high-priced Luwak coffee with regular coffee green beans. However the absence of tools and methods to classify Luwak coffee counterfeiting makes the sensing methods development urgent. The research aimed to detect and classify Luwak coffee green beans purity into the following purity categories very low (0-25) low (25-50) medium (50-75) and high (75-100). The classifying method relied on a low-cost commercial visible light camera and the deep learning model method. Then the research also compared the performance of four pre-trained convolutional neural network (CNN) models consisting of SqueezeNet GoogLeNet ResNet-50 and AlexNet. At the same time the sensitivity analysis was performed by setting the CNN parameters such as optimization technique (SGDm Adam RMSProp) and the initial learning rate (0.00005 and 0.0001). The training and validation result obtained the GoogLeNet as the best CNN model with optimizer type Adam and learning rate 0.0001 which resulted in 89.65 accuracy. Furthermore the testing process using confusion matrix from different sample data obtained the best CNN model using ResNet-50 with optimizer type RMSProp and learning rate 0.0001 providing an accuracy average of up to 85.00. Later the CNN model can be used to establish a real-time non-destructive rapid and precise purity detection system.

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Abstract

Luwak coffee (palm civet coffee) is known as one of the most expensive coffee in the world. In order to lower production costs Indonesian producers and retailers often mix high-priced Luwak coffee with regular coffee green beans. However the absence of tools and methods to classify Luwak coffee counterfeiting makes the sensing methods development urgent. The research aimed to detect and classify Luwak coffee green beans purity into the following purity categories very low (0-25) low (25-50) medium (50-75) and high (75-100). The classifying method relied on a low-cost commercial visible light camera and the deep learning model method. Then the research also compared the performance of four pre-trained convolutional neural network (CNN) models consisting of SqueezeNet GoogLeNet ResNet-50 and AlexNet. At the same time the sensitivity analysis was performed by setting the CNN parameters such as optimization technique (SGDm Adam RMSProp) and the initial learning rate (0.00005 and 0.0001). The training and validation result obtained the GoogLeNet as the best CNN model with optimizer type Adam and learning rate 0.0001 which resulted in 89.65 accuracy. Furthermore the testing process using confusion matrix from different sample data obtained the best CNN model using ResNet-50 with optimizer type RMSProp and learning rate 0.0001 providing an accuracy average of up to 85.00. Later the CNN model can be used to establish a real-time non-destructive rapid and precise purity detection system.

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Item Type:	Article
AGROVOC Term:	Coffee
AGROVOC Term:	Mixing
AGROVOC Term:	Classification
AGROVOC Term:	Application methods
AGROVOC Term:	Classification systems
AGROVOC Term:	Purity
Depositing User:	Mr. AFANDI ABDUL MALEK
Last Modified:	24 Apr 2025 00:55
URI:	http://webagris.upm.edu.my/id/eprint/10275

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