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All Journal International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Journal TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat Jurnal Penelitian Enjiniring Fakultas Teknik Unhas JURNAL SIPISSANGNGI: Jurnal Pengabdian Kepada Masyarakat
Areni, Intan Sari
Hasanuddin University
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Engineering Public Health Other

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Cocoa bean quality identification using a computer vision-based color and texture feature extraction Basri, Basri; Indrabayu, Indrabayu; Achmad, Andani; Areni, Intan Sari
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1609

Abstract

The current pressing issue in the downstream processing of cocoa beans in cocoa production is a strict quality control system. However, visually inspecting raw cocoa beans reveals the need for advanced technological solutions, especially in Industry 4.0. This paper introduces an innovative image-processing approach to extracting color and texture features to identify cocoa bean quality. Image acquisition involved capturing video with a data acquisition box device connected to a conveyor, resulting in image samples of Good-quality and Poor-quality of non-cutting cocoa beans dataset. Our methodology includes multifaceted advanced pre-processing, sharpening techniques, and comparative analysis of feature extraction methodologies using Hue-Saturation-Value (HSV) and Gray Level Cooccurrence Matrix (GLCM) with correlated features. This study used 15 features with the highest correlation. Machine Learning models using Support Vector Machine (SVM) with some parameter variation value alongside an RBF kernel. Some parameters were measured to compare each approach, and the results show that pre-processing without sharpening achieves better accuracy, notably with the HSV and GLCM combination reaching 0.99 accuracy. Adequate technical lighting during data acquisition is crucial for accuracy. This study sheds light on the efficacy of image processing in cocoa bean quality identification, addressing a critical gap in industrial-scale implementation of technological solutions and advancing quality control measures in the cocoa industry.
Co-Authors ., Dewiani ., Wardi Adnan Adnan Ahmad, Andani Akil, Yusri Syam Al Ghozy, Osama Iyad Alimuddin, A.Ais Prayogi Amil Ahmad Ilham Amirullah, Indrabayu Andani Achmad Andini Dani Achmad, Andini Dani Anshar, Muh Arifin, Nurhikma Basri Basri Bustamin, Anugrahyani Bustamin, Anugrayani Dewiani Dewiani Eka Qadri Nuranti Elly Warni Fitriyanti Mayasari Ida Ayu Putu Sri Widnyani Ifwan, Ichsanul Al Ikhlas Kitta Indrabayu . Indrabayu Indrabayu Ingrid Nurtanio Jefri, Muhammad Rivaldi Lubis, Nurlatifah Mahdaniar, Mahdaniar Marindah, Tyanita Puti Mokobombang, Novy Nur R A Mokobombang, Novy Nur R.A Musyfirah, Kamtina Naili Suri Intizhami Nur Hikmah Nurdin, Arliyanti Nurdin, Winati Mutmainnah Nurjannah . Palantei, Elyas Paundu, Ady Wahyudi Putri Akhmad, Ummu Qalsum Raden Wirawan Rahayu, Mesra RIFALDI, AHMAD Salam, Ejah Umraeni Salama Manjang Suandi, Ahmad Yusuf Syafaruddin Syafaruddin Tassakka, Muhammad Irpan Sejati Wardi Wardi Yohannes, Christoforus Yohannes, Chystoporus Yudha, Muh. Reza Eka Yusuf, Mukarramah Zaenab Muslimin Zahir Zainuddin Zulkifli Tahir