Quality and Defect Inspection of Green Coffee Beans Using a Computer Vision System
Source abstract
Abstract
There is an increased industry demand for efficient and safe methods to select the best-quality coffee beans for a demanding market. Color, morphology, shape and size are important factors that help identify the best quality beans; however, conventional techniques based on visual and/or mechanical inspection are not sufficient to meet the requirements. Therefore, this paper presents an image processing and machine learning technique integrated with an Arduino Mega board, to evaluate those four important factors when selecting best-quality green coffee beans. For this purpose, the k-nearest neighbor algorithm is used to determine the quality of coffee beans and their corresponding defect types. The system consists of logical processes, image processing and the supervised learning algorithms that were programmed with MATLAB and then burned into the Arduino board. The results showed this method has a high effectiveness in classifying each single green coffee bean by identifying its main visual characteristics, and the system can handle several coffee beans present in a single image. Statistical analysis shows the process can identify defects and quality with high accuracy. The artificial vision method was helpful for the selection of quality coffee beans and may be useful to increase production, reduce production time and improve quality control.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Spectroscopy and Chemometric Analyses · Smart Agriculture and AI · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Science, School of Physics, Universidad Nacional de Colombia-Sede Medellín, Carrera 65 Nro. 59A-110, Medellín 050034, Colombia
Faculty of Mines, Department of Electrical Energy and Automation, Universidad Nacional de Colombia-Sede Medellín, Carrera 80 No 65-223, Campus Robledo, Medellín 050041, Colombia
References (38)
Show source-provided references
1. Common roasting defects in coffee: Aroma composition, sensory characterization and consumer perception10.1016/j.foodqual.2018.03.009 ↗
2. Evolution of sensory aroma attributes from coffee beans to brewed coffee10.1016/j.lwt.2011.07.001 ↗
3. Machine vision system for food grain quality evaluation: A review10.1016/j.tifs.2016.07.011 ↗
4. A real-time mathematical computer method for potato inspection using machine vision10.1016/j.camwa.2011.11.019 ↗
5. Machine vision system for grading of dried figs10.1016/j.compag.2015.10.019 ↗
6. Recognition of coffee roasting degree using a computer vision system10.1016/j.compag.2018.11.029 ↗
7. Non-destructive and contactless quality evaluation of table grapes by a computer vision system10.1016/j.compag.2018.12.019 ↗
8. Potato quality grading based on machine vision and 3D shape analysis10.1016/j.compag.2018.07.012 ↗
9. Artificial Vision Techniques to Optimize Strawberry’s Industrial Classification10.1109/TLA.2016.7555221 ↗
10. Machine vision based soybean quality evaluation10.1016/j.compag.2017.06.023 ↗
11. Habib, M.T., Majumder, A., Jakaria, A.Z.M., Akter, M., Uddin, M.S., and Ahmed, F. (2018). Machine vision based papaya disease recognition. J. King Saud Univ. Comput. Inf. Sci.
12. Design of an automatic apple sorting system using machine vision10.1016/j.compag.2016.06.030 ↗
13. Classification of pepper seeds using machine vision based on neural network
14. Non-destructive evaluation of quality and ammonia content in whole and fresh-cut lettuce by computer vision system10.1016/j.foodres.2014.07.037 ↗
15. Recent developments and applications of image features for food quality evaluation and inspection—A review10.1016/j.tifs.2006.06.005 ↗
16. Leaf classification in sunflower crops by computer vision and neural networks10.1016/j.compag.2011.05.007 ↗
17. Feature decision-making ant colony optimization system for an automated recognition of plant species10.1016/j.eswa.2014.11.011 ↗
18. Apple detection during different growth stages in orchards using the improved YOLO-V3 model10.1016/j.compag.2019.01.012 ↗
19. Quality assessment of blueberries by computer vision10.1016/j.profoo.2011.09.065 ↗
20. A computer vision system for coffee beans classification based on computational intelligence techniques10.1016/j.jfoodeng.2015.10.009 ↗
21. Clasificación de los frutos de café según su estado de maduración y detección de la broca mediante técnicas de procesamiento de imágenes10.15665/rp.v14i1.640 ↗
22. Identificación y clasificación de frutos de café en tiempo real a través de la medición de color
23. Clasificación de Granos de Café usando FPGA
24. Pinto, C., Furukawa, J., Fukai, H., and Tamura, S. (2017, January 16–18). Classification of Green coffee bean images basec on defect types using convolutional neural network (CNN). Proceedings of the 2017 International Conference on Advanced Informatics, Concepts, Theory, and Applications (ICAICTA), Denpasar, Indonesia.10.1109/ICAICTA.2017.8090980 ↗
25. Arboleda, E.R., Fajardo, A.C., and Medina, R.P. (2018, January 11–12). Classification of coffee bean species using image processing, artificial neural network and k nearest neighbors. Proceedings of the 2018 IEEE International Conference on Innovative Research and Development (ICIRD), Bangkok, Thailand.10.1109/ICIRD.2018.8376326 ↗
26. Condori, R.H.M., Humari, J.H.C., Portugal-Zambrano, C.E., Gutierrez-Caceres, J.C., and Beltran-Castanon, C.A. (2014, January 15–19). Automatic classification of physical defects in green coffee beans using CGLCM and SVM. Proceedings of the 2014 XL Latin American Computing Conference (CLEI), Montevideo, Uruguay.
27. Arboleda, E.R., Fajardo, A.C., and Medina, R.P. (2018, January 11–12). An image processing technique for coffee black beans identification. Proceedings of the 2018 IEEE International Conference on Innovative Research and Development (ICIRD), Bangkok, Thailand.10.1109/ICIRD.2018.8376325 ↗
28. Apaza, R.G., Portugal-Zambrano, C.E., Gutierrez-Caceres, J.C., and Beltran-Castanon, C.A. (2014, January 15–19). An approach for improve the recognition of defects in coffee beans using retinex algorithms. Proceedings of the 2014 XL Latin American Computing Conference (CLEI), Montevideo, Uruguay.10.1109/CLEI.2014.6965102 ↗
29. Composition of green and roasted coffees of different cup qualities10.1016/j.lwt.2004.08.014 ↗
30. Bhargava, A., and Bansal, A. (2018). Fruits and vegetables quality evaluation using computer vision: A review. J. King Saud Univ. Comput. Inf. Sci.
31. Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review10.1016/j.compag.2018.08.001 ↗
32. Visual quality detection of aquatic products using machine vision10.1016/j.aquaeng.2014.10.003 ↗
33. Machine Vision-Based Measurement Systems for Fruit and Vegetable Quality Control in Postharvest
34. Current and future applications of statistical machine learning algorithms for agricultural machine vision systems10.1016/j.compag.2018.12.006 ↗
35. Efficient kNN Classification With Different Numbers of Nearest Neighbors10.1109/TNNLS.2017.2673241 ↗
36. Singh, A., and Pandey, B. (2016, January 26–27). An euclidean distance based KNN computational method for assessing degree of liver damage. Proceedings of the 2016 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India.10.1109/INVENTIVE.2016.7823222 ↗
37. A wrapper-based approach to image segmentation and classification10.1109/TIP.2005.859374 ↗
38. Arias, M., Manuel, A., and Sierra Ruiz, J.A. Procesamiento de imágenes para la clasificación de café verde. [Bachelor’s Thesis, Pontificia Universidad Javeriana].