Review Identifikasi dan Klasifikasikan Biji Kopi Menggunakan Computer Vision
DOI:
https://doi.org/10.26418/justin.v11i2.54925Keywords:
Machine Learning, Neural Network, Computer VisionAbstract
Dianggap sebagai salah satu minuman terpenting yang ada saat ini, Kopi banyak dikonsumsi di seluruh dunia. Beberapa faktor yang mempengaruhi kualitas biji kopi seperti warna, tekstur, ukuran, aroma, dll serta proses lain di sepanjang rantai produksi seperti proses penanaman, pemanggangan, dan penggilingan. Namun semua proses tersebut akan sia-sia jika kualitas biji kopinya rendah. Jadi, sangatlah penting untuk hanya menggunakan biji kopi dengan kualitas terbaik. Oleh karena itu, tantangannya adalah mengembangkan sistem yang menggunakan visi komputer untuk mengidentifikasi biji berkualitas tinggi atau mengklasifikasi berdasarkan spesiesnya untuk memudahkan upaya yang diperlukan oleh semua pelaku dalam rantai pasokan. Memberikan informasi kepada pelanggan akhir juga bisa menjadi faktor penentu untuk memajukan industri kopi. Makalah ini bertujuan untuk meninjau literatur dalam topik menggunakan visi komputer untuk biji kopi. Setelah meninjau sejumlah studi yang dipilih yang sesuai dengan topik yang dipilih dalam makalah kami, teknik visi komputer digunakan untuk dua alasan utama, identifikasi dan klasifikasi. Penelitian tentang topik ini masih terbatas. Oleh karena itu, dapat disimpulkan bahwa masih banyak ruang untuk mempelajari topik ini. Penelitian ini juga bertujuan untuk membantu memberikan bahan penelitian bagi peneliti selanjutnya.References
E. R. Arboleda, A. C. Fajardo, and R. P. Medina, “An image processing technique for coffee black beans identification,†in 2018 IEEE International Conference on Innovative Research and Development, ICIRD 2018, 2018.
C. Yeretzian, “Coffee,†in Springer Handbooks, 2017.
E. M. De Oliveira, D. S. Leme, B. H. G. Barbosa, M. P. Rodarte, and R. G. F. Alvarenga Pereira, “A computer vision system for coffee beans classification based on computational intelligence techniques,†J. Food Eng., 2016.
D. S. Leme, S. A. da Silva, B. H. G. Barbosa, F. M. Borém, and R. G. F. A. Pereira, “Recognition of coffee roasting degree using a computer vision system,†Comput. Electron. Agric., 2019.
T. H. Nasution and U. Andayani, “Recognition of roasted coffee bean levels using image processing and neural network,†in IOP Conference Series: Materials Science and Engineering, 2017.
E. R. Arboleda, A. C. Fajardo, and R. P. Medina, “Classification of coffee bean species using image processing, artificial neural network and K nearest neighbors,†in 2018 IEEE International Conference on Innovative Research and Development, ICIRD 2018, 2018.
C. Pinto, J. Furukawa, H. Fukai, and S. Tamura, “Classification of Green coffee bean images basec on defect types using convolutional neural network (CNN),†in Proceedings - 2017 International Conference on Advanced Informatics: Concepts, Theory and Applications, ICAICTA 2017, 2017.
U. Lehtinen, “Traceability in Agrifood Chains,†Intell. Agrifood Chain. Networks, pp. 151–166, 2011.
S. Khan, A. Haleem, M. I. Khan, M. H. Abidi, and A. Al-Ahmari, “Implementing traceability systems in specific supply chain management (SCM) through critical success factors (CSFs),†Sustain., 2018.
M. M. Aung and Y. S. Chang, “Traceability in a food supply chain: Safety and quality perspectives,†Food Control, vol. 39, no. 1, pp. 172–184, 2014.
B. Kitchenham and P. Brereton, “A systematic review of systematic review process research in software engineering,†Information and Software Technology. 2013.
J. Cruz-Benito, “Systematic Literature Review & Mapping,†2016, p. 67.
H. Steinhart, “Coffee: growing, processing, sustainable production—a guidebook for growers, processors, traders, and researchers. Jean Nicolas Wintgens (Ed). Wiley-VCH Verlag, Weinheim, 2004. 976 pp, ISBN 3-527-30731-1,†J. Sci. Food Agric., vol. 85, no. 11, 2005.
S. Schenker and T. Rothgeb, “The roast—Creating the Beans’ signature,†Cr. Sci. coffee, 2017.
C. Lambot, J. C. Herrera, B. Bertrand, S. Sadeghian, P. Benavides, and A. Gaitán, “Cultivating Coffee Quality-Terroir and Agro-Ecosystem,†in The Craft and Science of Coffee, 2017.
M. D. del Castillo, B. Fernandez-Gomez, N. Martinez-Saez, A. Iriondo, and M. D. Mesa, Coffee By-Products. 2018.
M. Castillo, B. Fernandez-Gomez, N. M. Sáez, and ..., Coffee by-products. digital.csic.es, 2019.
Y. C. Chou et al., “Deep-learning-based defective bean inspection with GAN-structured automated labeled data augmentation in coffee industry,†Appl. Sci., vol. 9, no. 19, 2019.
J. Kosalos, R. Stephen, S. Diaz, P. Songer, and M. Alves, “SCAA Arabica Green Coffee Defect Handbook,†Spec. Coffee Assoc. Am. SCAA, 2006.
C. R. Institute, “SCAA Coffee Beans Classification,†2006. [Online]. Available: http://www.coffeeresearch.org/coffee/scaaclass.htm. [Accessed: 22-Feb-2022].
D. S. Kalel, P. M. Pisal, and R. P. Bagawade, “A Study of Color, Shape and Texture Feature Extraction for Content Based Image Retrieval System,†Int. J. Adv. Res. Comput. Commun., 2016.
F. Alamdar and M. R. Keyvanpour, “A new color feature extraction method based on quad histogram,†in Procedia Environmental Sciences, 2011.
K. León, D. Mery, F. Pedreschi, and J. León, “Color measurement in L*a*b* units from RGB digital images,†Food Res. Int., 2006.
P. Wang, H. W. Tseng, T. C. Chen, and C. H. Hsia, “Deep convolutional neural network for coffee bean inspection,†Sensors Mater., vol. 33, no. 7, pp. 2299–2310, 2021.
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