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Implementation of Grading Method for Gambier Leaves Based on Combination of Area, Perimeter, and Image Intensity Using Backpropagation Artificial Neural Network

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Electrical Engineering Department, Faculty of Engineering, Universitas Andalas, Padang City 25163, Indonesia
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Computer System Department, Faculty of Information System, Universitas Andalas, Padang City 25163, Indonesia
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Agriculture Engineering Department, Faculty of Agriculture Technology, Universitas Andalas, Padang City 25163, Indonesia
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Physics Department, Faculty of Science, National University of Singapore, Singapore 117546, Singapore
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Author to whom correspondence should be addressed.
Electronics 2019, 8(11), 1308; https://doi.org/10.3390/electronics8111308
Received: 3 October 2019 / Revised: 29 October 2019 / Accepted: 4 November 2019 / Published: 7 November 2019
(This article belongs to the Section Computer Science & Engineering)
Gambier leaves are widely used in cosmetics, beverages, and medicine. Tarantang village in West Sumatera, Indonesia, is famous for its gambier commodity. Farmers usually classify gambier leaves by area and color. They inherit this ability through generations. This research creates a tool to imitate the skill of the farmers to classify gambier leaves. The tool is a box covered from outside light. Two LEDs are attached inside the box to get maintain light intensity. A camera is used to capture the leaf image and a raspberry Pi processes the leaf features. A mini monitor is provided to operate the system. Six hundred and twenty-five gambier leaves were classified into five grades. Leaves categorized into grades 1, 2, and 3 are forbidden to be picked. Grade 4 leaves are allowed to be picked and those in grade 5 are the recommended ones for picking. Leaf features are area, perimeter, and intensity of leaf image. Three artificial neural networks are developed based on each feature. One thousand leaf images were used for training and 500 leaf images were used for testing. The accuracies of the features are about 93%, 96% and 97% for area, perimeter and intensity, respectively. A combination of rules are introduced into the system based on the feature accuracy. Those rules can give 100% accuracy compared to the farmer’s recommendation. A real time application to classify the leaves could provide classification with the same decision result as the classifying performed by the farmers. View Full-Text
Keywords: gambier leaf; grade; neural network gambier leaf; grade; neural network
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Rusydi, M.I.; Anandika, A.; Rahmadya, B.; Fahmy, K.; Rusydi, A. Implementation of Grading Method for Gambier Leaves Based on Combination of Area, Perimeter, and Image Intensity Using Backpropagation Artificial Neural Network. Electronics 2019, 8, 1308.

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