Jelita Asian, Nunik Destria Arianti, Ariefin, Muhamad Muslih
The low-cost camera for machine vision, such as a webcam, still has a problem with resolution noise. Therefore, it is important to learn strategies to reduce noise from low-cost camera images so that they can be widely used for grading machines in the future. This paper aims to compare three feature extraction methods with auto-preprocessing to classify chili pepper (Capsicum Frutescens) quality using a machine learning algorithm. Three extraction methods were used, including the color feature, oriented FAST and rotated BRIEF (ORB), and the combination color feature and ORB. A total of 525 image data for quality chili pepper were collected using the webcam. The auto-preprocessing strategy to classify chili peppers can improve the performance of machine-learning algorithms for all data generated by the feature extractor. The performance of the chili paper quality classification model with auto-preprocessing of the variable color feature can improve the performance of machine learning algorithms by up to 64.21%. The performance improvement of the classification model using the ORB feature variable and the auto-preprocessing of up to 4.41%. The performance improvement of the classification model using machine learning algorithms is 11.27% when using the combination color feature and ORB feature and auto-preprocessing. © 2024, Institute of Advanced Engineering and Science. All rights reserved.
Department of Informatic Magister, Nusa Putra University, Sukabumi, Indonesia; Department of Information System, Nusa Putra University, Sukabumi, Indonesia; Department of Mechanical Engineering, Lhokseumawe State Polytechnic, Lhokseumawe, Indonesia
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