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All Journal Indonesian Applied Physics Letters Journal of Educational Engineering and Environment Science and Technology: Jurnal Pengabdian Masyarakat
Imron, Arizal Mujibtamana Nanda
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences Other

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Journal : Journal of Educational Engineering and Environment

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Application of Computer Vision for Digital Encyclopedia of Chili Varieties (Capsicum spp.) Zamzami, Muhammad Viqih; Fitri, Zilvanhisna Emka; Madjid, Abdul; Imron, Arizal Mujibtamana Nanda
Journal of Educational Engineering and Environment Vol. 3 No. 1 (2024): Journal of Educational Engineering and Environment
Publisher : Fakultas Teknik Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/jeee.v3i1.3831

Abstract

Chili is a vegetable commodity that has high economic value so that its production always increases every year. Several types of chilies are cultivated in Indonesia, namely cayenne pepper (Capsicum frutescens), curly red chili (Capsicum annuum L. var. longum), large red chili (Capsicum annuum L.) and paprika (Capsicum annuum var. grossum). Indonesia is a country that continues to develop innovation to produce many superior varieties, especially chilli plants. The problem arises that variations of other superior chili varieties can only be accessed through the official website of the ministry of agriculture, however, the data that can be accessed is limited (in the form of descriptions of chili varieties without physical appearance such as photos) so that it is quite difficult for the community and farmers to cultivate or utilize these varieties. This made researchers develop a digital encyclopedia of chili types using computer vision. This study uses a combination of digital image processing and intelligent systems. The image processing used is preprocessing such as cropping and splitting of RGB components, image segmentation and shape feature extraction. The features used are area, perimeter, major axis length, minor axis length and eccentricity. This feature is the input of the Naïve Bayes method which produces a system accuracy rate of 92%.
Co-Authors Abdul Madjid Fitri, Zilvanhisna Emka Hasan, Baharuddin Zamzami, Muhammad Viqih