Identification of Philippine Chili Peppers using Canny Edge Detection and Feed – Forward Artificial Neural Network

Cristine Dominic Cabalar, Shayne Marie Cleofas,Meo Vincent Caya

2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)(2022)

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摘要
Chili peppers are well – known used ingredients in the Philippines and are used to prepare different recipes for sauces, pickles, and as a flavoring ingredient. The varieties of chili peppers locally available in the Philippines are siling labuyo, siling haba, siling tingala, and siling makopa. Some studies classify the maturity of a certain variety of chili, but there are few and close to no studies that identify the type of Philippine chili peppers. To solve the problem, the researchers developed a system such as the canny edge detection and feed-forward artificial neural network that will capture the image of the chili pepper sample. The research is limited to the varieties of chili peppers such as siling labuyo, siling haba, siling tingala, and siling makopa. The type of image the researchers will be using is JPEG. Thus, the study does not detect defects and damages of the identified chili pepper. From the results, it is concluded that the system has an accuracy of 90.0%.
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关键词
Canny Edge Detection,Feed – Forward Artificial Neural Network,Image Processing,Philippine Chili Peppers
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