Normal and Peaberry Coffee Beans Classification from Green Coffee Bean Images Using Convolutional Neural Networks and Support Vector Machine
The aim of this study is to develop a system which can identify and sort peaberries automatically at low cost for coffee producers in developing countries. In this paper, the focus is on the classification of peaberries and normal coffee beans using image processing and machine learning techniques. The peaberry is not bad and not a normal bean. The peaberry is born in an only single seed, relatively round seed from a coffee cherry instead of the usual flat-sided pair of beans. It has another value and flavor. To make the taste of the coffee better, it is necessary to separate the peaberry and normal bean before green coffee beans roasting. Otherwise, the taste of total beans will be mixed, and it will be bad. In roaster procedure time, all the beans shape, size, and weight must be unique; otherwise, the larger bean will take more time for roasting inside. The peaberry has a different size and different shape even though they have the same weight as normal beans. The peaberry roasts slower than other normal beans. Therefore, neither technique provides a good option to select the peaberries. Defect beans, e.g., sour, broken, black, and fade bean, are easy to check and pick up manually by hand. On the other hand, the peaberry pick up is very difficult even for trained specialists because the shape and color of the peaberry are similar to normal beans. In this study, we use image processing and machine learning techniques to discriminate the normal and peaberry bean as a part of the sorting system. As the first step, we applied Deep Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) as machine learning techniques to discriminate the peaberry and normal bean. As a result, better performance was obtained with CNN than with SVM for the discrimination of the peaberry. The trained artificial neural network with high performance CPU and GPU in this work will be simply installed into the inexpensive and low in calculation Raspberry Pi system. We assume that this system will be used in under developed countries. The study evaluates and compares the feasibility of the methods in terms of accuracy of classification and processing speed.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1346
 Giacalone, D.; Degn, T.K.; Yang, N.; Liu, C.; Fisk, I.; Münchow, M. Common roasting defects in coffee: Aroma composition, sensory characterization and consumer perception. Food Qual. Prefer. 2019, 71, 463–474. (CrossRef).
 Bhumiratana, N.; Adhikari, K.; Chambers, E. Evolution of sensory aroma attributes from coffee beans to brewed coffee. LWT Food Sci. Technol. 2011, 44, 2185–2192. (CrossRef).
 Vithu, P.; Moses, J.A. Machine vision system for food grain quality evaluation: A review. Trends Food Sci. Technol. 2016, 56, 13–20. (CrossRef).
 García, M., Candelo-Becerra, J. E., & Hoyos, F. E. (2019). Quality and Defect Inspection of Green Coffee Beans Using a Computer Vision System. Applied Sciences, 9(19), 4195.
 Peaberry coffee information Available at: https://gamblebaycoffee.com/peaberry-coffee-beans-better-regular/, Accessed 18th November 2019
 Diding Suhandy & Meinilwita Yulia (2017) Peaberry coffee discrimination using UV-visible spectroscopy combined with SIMCA and PLS-DA, International Journal of Food Properties, 20:sup1, S331-S339, DOI: 10.1080/10942912.2017.1296861
 Peaberry features information Available at https://coffeebrat.com/peaberry-coffee/, Accessed 20th November 2019
 Peaberry abnormality information Available from: https://coffee.fandom.com/wiki/Peaberry, Accessed 18th November 2019
 H. Fukai, J. Furukawa, Hiroki Katsuragawa, C. Pinto, Carmelita Afonso, Dili, Timor-Leste. "Classification of Green Coffee Beans by Convolutional Neural Network and its Implementation on Raspberry Pi and Camera Module".
 C. Pinto, J. Furukawa, H. Fukai, and S. Tamura, “Classification of Green Coffee Bean Images Based on Defect Types Using Convolutional Neural Network (CNN)”, Advanced Informatics, Concepts, Theory, and Applications (ICAICTA), 2017 International Conference on. IEEE, pp.1– 5. 2017.
 Suhandy, Diding and Yulia, Meinilwita and Kusumiyati, “Chemometric quantification of peaberry coffee in blends using UV–visible spectroscopy and partial least squares regression”. AIP Conference Proceedings 2021, 060010 (2018) URL: https://aip.scitation.org/doi/abs/10.1063/1.5062774
 Peaberry coffee bean information Available at https://coffee-brewing-methods.com/coffee-beans-review/peaberry-coffee/, Accessed 10th December 2019
 Mezghani, D. Ben Ayed, S. Zribi Boujelbene, and N. Ellouze. "Evaluation of SVM kernels and conventional machine learning algorithms for speaker identification." International journal of Hybrid information technology 3.3 (2010): 23-34.
 Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp.2278–2324, 1998.
 Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol.521, pp.436–444, May 2015.
 Visen, N.S., J. Paliwal, D.S. Jayas, and N.D.G. White: Image analysis of bulk grain samples using neural networks. Canadian Biosystems Engineering 46:711-715, 2004.
 Liu, Zhao-yan, Cheng Fang, Ying Yi-bin and Rao Xiu-qin: Identification of Rice Varieties Using Nueral Networks. Journal of Zhejiang University Science, 6B (11):1095-1100, 2005.
 Visen, N.S., J. Paliwal, D.S. Jayas, and N.D.G. White: Specialist Neural Networks for Cereal Grain Classi10fication, Bio systems Engineering 82 (2): 151–159, 2002.
 Raji, A. O. and A. O. Alamutu: Prospects of Computer Vision Automated Sorting Systems in Agricultural Process Operations in Nigeria. Agricultural Engineering International: The CIGR Journal of Scientific Research and Development Vol. VII. Invited Overview, 2005.
 Deshmukh, K.S. and G. N. Shinde: An Adaptive Color Image Segmentation, Electronic Letters on Computer Vision and Image Analysis 5(4):12-23, 2005.
 Jayas, D. S., J. Paliwal and N. S. Visen: Multi-layer Neural Networks for Image Analysis of Agricultural Products, Journal of Agricultural Engineering Research. 77(2):119-128, 2000.
 Junlong Fang, Shuwen Wang and Changli Zhang: Genetic Algorithm Trained Artificial Neural Network, Nature and Science, 2005.
 Ding, K. and S. Gunasekaran: Shape feature extraction and Classification of food Material Using Computer Vision, Food and Process Engineering Inst. of ASAE, 1994.
 http://www.coffeeanalysts.com: Accessed 10th November 2019
 António L. Amaral, Orlando Rocha1, Cristina Gonçalves, António Augusto Ferreira and Eugénio C. Ferreira: Development of Image Analysis Methods to Evaluate 93 Barley/ Malt Grain Size, 1999.
 Yang, C.C., S.O. Prasher, J.A. Landry, H.S Ramaswamy and A. Ditommaso: Application of artificial neural networks in image recognition and classification of crop and weeds, 2000.
 Kavdir, I. and D. E. Guyer: Apple Grading Using Fuzzy Logic, Journal of Agricultural Engineering Research. 27:375-382, 2003.
 Coffee information Available from https://www.zecuppa.com/coffeeterms-bean-defects.htm, Accessed 12th December 2019
 CNN information Available at https://www.techopedia.com/definition/32731/convolutional-neural-network-cnn, Accessed 10th December 2019
 M. Bak, “Support Vector Classifier with Linguistic Interpretation of the Kernel Matrix in Speaker Verification”, Man-Machine Interactions, Krzysztof A. Cyran, Stanislaw Kozielski, James F. Peters (eds.), ISSN 1867-5662, Springer, 2009, pp 399-406.
 N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. JMLR, 2014.
 Chainer information Available from https://chainer.org/, Accessed 10th December 2019
 Bahari, Nurul Iman Saiful, Asmala Ahmad, and Burhanuddin Mohd Aboobaider. "Application of support vector machine for classification of multispectral data." IOP Conference Series: Earth and Environmental Science. Vol. 20. No. 1. IOP Publishing, 2014.
 Deep Learning information Available at: https://mila.quebec/en/publication/deep-learning/, Accessed 18th November 2019