{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T05:35:15Z","timestamp":1773552915876,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T00:00:00Z","timestamp":1634601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Aquatic products are popular among consumers, and their visual quality used to be detected manually for freshness assessment. This paper presents a solution to inspect tuna and salmon meat from digital images. The solution proposes hardware and a protocol for preprocessing images and extracting parameters from the RGB, HSV, HSI, and L*a*b* spaces of the collected images to generate the datasets. Experiments are performed using machine learning classification methods. We evaluated the AutoML models to classify the freshness levels of tuna and salmon samples through the metrics of: accuracy, receiver operating characteristic curve, precision, recall, f1-score, and confusion matrix (CM). The ensembles generated by AutoML, for both tuna and salmon, reached 100% in all metrics, noting that the method of inspection of fish freshness from image collection, through preprocessing and extraction\/fitting of features showed exceptional results when datasets were subjected to the machine learning models. We emphasize how easy it is to use the proposed solution in different contexts. Computer vision and machine learning, as a nondestructive method, were viable for external quality detection of tuna and salmon meat products through its efficiency, objectiveness, consistency, and reliability due to the experiments\u2019 high accuracy.<\/jats:p>","DOI":"10.3390\/informatics8040070","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T22:07:04Z","timestamp":1634767624000},"page":"70","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Computer Vision and Machine Learning for Tuna and Salmon Meat Classification"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2506-7116","authenticated-orcid":false,"given":"Erika Carlos","family":"Medeiros","sequence":"first","affiliation":[{"name":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, University City, Recife 50.740-560, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8025-0517","authenticated-orcid":false,"given":"Leandro Maciel","family":"Almeida","sequence":"additional","affiliation":[{"name":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, University City, Recife 50.740-560, Brazil"}]},{"given":"Jos\u00e9 Gilson de Almeida Teixeira","family":"Filho","sequence":"additional","affiliation":[{"name":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, University City, Recife 50.740-560, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,19]]},"reference":[{"key":"ref_1","unstructured":"Sun, D.-W. (2016). Computer Vision Technology for Food Quality Evaluation, Academic Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/S0924-2244(97)01049-2","article-title":"Methods to evaluate fish freshness in research and industry","volume":"8","author":"Olafsdottir","year":"1997","journal-title":"Trends Food Sci. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.jfoodeng.2015.12.018","article-title":"Image processing based method to assess fish quality and freshness","volume":"177","author":"Dutta","year":"2016","journal-title":"J. Food Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1300\/J030v09n03_02","article-title":"A critical look at whether freshness can be determined","volume":"9","author":"Bremner","year":"2000","journal-title":"J. Aquat. Food Prod. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Szeliski, R. (2010). Computer Vision: Algorithms and Applications, Springer Science & Business Media.","DOI":"10.1007\/978-1-84882-935-0"},{"key":"ref_6","unstructured":"Alpaydin, E. (2020). Introduction to Machine Learning, MIT Press."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/S0168-1699(02)00101-1","article-title":"Inspection and grading of agricultural and food products by computer vision systems\u2014A review","volume":"36","author":"Brosnan","year":"2002","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","first-page":"167","article-title":"Machine vision applications to aquatic foods: A review","volume":"11","author":"Balaban","year":"2011","journal-title":"Turk. J. Fish. Aquat. Sci."},{"key":"ref_9","unstructured":"Hanson, A. (1978). Computer Vision Systems, Elsevier."},{"key":"ref_10","first-page":"127","article-title":"Standard RGB color spaces","volume":"Volume 1999","author":"Buckley","year":"1999","journal-title":"Color and Imaging Conference"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1071\/EG992219","article-title":"Pixel map preparation using the HSV color model","volume":"23","author":"Milligan","year":"1992","journal-title":"Explor. Geophys."},{"key":"ref_12","unstructured":"Welch, E., Moorhead, R., and Owens, J.K. (1991, January 7\u201310). Image processing using the HSI color space. Proceedings of the IEEE Proceedings of the SOUTHEASTCON91, Williamsburg, VA, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1109\/83.597279","article-title":"A study of efficiency and accuracy in the transformation from RGB to CIELAB color space","volume":"6","author":"Connolly","year":"1997","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"106622","DOI":"10.1016\/j.knosys.2020.106622","article-title":"AutoML: A survey of the state-of-the-art","volume":"212","author":"He","year":"2021","journal-title":"Knowl. Based Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Trientin, D., Hidayat, B., and Darana, S. (2015, January 29\u201330). Beef freshness classification by using color analysis, multi-wavelet transformation, and artificial neural network. Proceedings of the 2015 International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), Bandung, Indonesia.","DOI":"10.1109\/ICACOMIT.2015.7440202"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kramer, O. (2013). K-nearest neighbors. Dimensionality Reduction with Unsupervised Nearest Neighbors, Springer.","DOI":"10.1007\/978-3-642-38652-7"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/101.8118","article-title":"Artificial neural networks","volume":"4","author":"Hopfield","year":"1988","journal-title":"IEEE Circuits Devices Mag."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jang, E., Cho, H., Kim, E.K., and Kim, S. (2015, January 18\u201320). Grade Prediction of Meat Quality in Korean Native Cattle Using Neural Network. Proceedings of the 2015 International Conference on Fuzzy Theory and Its Applications (iFUZZY), Yilan, Taiwan.","DOI":"10.1109\/iFUZZY.2015.7391889"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Adi, K., Pujiyanto, S., Nurhayati, O.D., and Pamungkas, A. (2015, January 2\u20133). Beef Quality Identification Using Color Analysis and K-Nearest Neighbor Classification. Proceedings of the 2015 4th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), Bandung, Indonesia.","DOI":"10.1109\/ICICI-BME.2015.7401359"},{"key":"ref_20","unstructured":"Gonzalez, R., Woods, R., and Eddins, S. (2020). Digital Image Processing Using MATLAB, Gatesmark. [3rd ed.]."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Winiarti, S., Azhari, A., and Agusta, K.M. (2018, January 29\u201330). Determining feasibility level of beef quality based on histogram and k-means clustering. Proceedings of the 2018 International Symposium on Advanced Intelligent Informatics (SAIN), Yogyakarta, Indonesia.","DOI":"10.1109\/SAIN.2018.8673366"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Altini, N., De Giosa, G., Fragasso, N., Coscia, C., Sibilano, E., Prencipe, B., Hussain, S.M., Brunetti, A., Buongiorno, D., and Guerriero, A. (2021). Segmentation and identification of vertebrae in CT scans using CNN, k-means clustering and k-NN. Informatics, 8.","DOI":"10.3390\/informatics8020040"},{"key":"ref_23","first-page":"281","article-title":"Learning the k in k-means","volume":"16","author":"Hamerly","year":"2004","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.compag.2018.07.031","article-title":"An embedded system based on DSP platform and PCA-SVM algorithms for rapid beef meat freshness prediction and identification","volume":"152","author":"Arsalane","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2812","DOI":"10.1039\/C3AY41907J","article-title":"Principal component analysis","volume":"6","author":"Bro","year":"2014","journal-title":"Anal. Methods"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/S0925-2312(03)00373-4","article-title":"Advanced support vector machines and kernel methods","volume":"55","year":"2003","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jfoodeng.2018.12.009","article-title":"An intelligent machine vision-based smartphone app for beef quality evaluation","volume":"248","author":"Hosseinpour","year":"2019","journal-title":"J. Food Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tan, W.K., Husin, Z., and Ismail, M.A.H. (2020, January 24\u201326). Feasibility study of beef quality assessment using computer vision and Deep Neural Network (DNN) algorithm. Proceedings of the 2020 8th International Conference on Information Technology and Multimedia (ICIMU), Selangor, Malaysia.","DOI":"10.1109\/ICIMU49871.2020.9243353"},{"key":"ref_29","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Friderikos, V. (2020). A survey of deep learning for data caching in edge network. Informatics, 7.","DOI":"10.3390\/informatics7040043"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e13039","DOI":"10.1111\/jfpe.13039","article-title":"A nondestructive intelligent approach to real-time evaluation of chicken meat freshness based on computer vision technique","volume":"42","author":"Fatahi","year":"2019","journal-title":"J. Food Process. Eng."},{"key":"ref_32","unstructured":"Wirsansky, E. (2020). Hands-on Genetic Algorithms with Python: Applying Genetic Algorithms to Solve Real-World Deep Learning and Artificial Intelligence Problems, Packt Publishing Ltd."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sun, X., Young, J., Liu, J.H., Chen, Q., and Newman, D. (2018). Predicting pork color scores using computer vision and support vector machine technology. Meat Muscle Biol.","DOI":"10.22175\/mmb2018.06.0015"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.compag.2019.02.023","article-title":"Real-time nondestructive monitoring of common carp fish freshness using robust vision-based intelligent modeling approaches","volume":"159","author":"Fatahi","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","first-page":"108","article-title":"A comparative study of artificial bee colony algorithm","volume":"214","author":"Karaboga","year":"2009","journal-title":"Appl. Math. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lugatiman, K., Fabiana, C., Echavia, J., and Adtoon, J.J. (December, January 29). Tuna meat freshness classification through computer vision. Proceedings of the 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Laoag, Philippines.","DOI":"10.1109\/HNICEM48295.2019.9073468"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Moon, E.J., Kim, Y., Xu, Y., Na, Y., Giaccia, A.J., and Lee, J.H. (2020). Evaluation of salmon, tuna, and beef freshness using a portable spectrometer. Sensors, 20.","DOI":"10.3390\/s20154299"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Albawi, S., Mohammed, T.A., and Al-Zawi, S. (2017, January 21\u201323). Understanding of a convolutional neural network. Proceedings of the 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey.","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"ref_39","unstructured":"Howse, J. (2013). OpenCV Computer Vision with Python, Packt Publishing Ltd."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jain, A., and Gupta, R. (2015, January 19\u201320). Gaussian Filter Threshold Modulation for Filtering Flat and Texture Area of an Image. Proceedings of the 2015 International Conference on Advances in Computer Engineering and Applications, Ghaziabad, India.","DOI":"10.1109\/ICACEA.2015.7164804"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1364\/JOSA.45.000546","article-title":"Some quantitative aspects of an opponent-colors theory. I. Chromatic responses and spectral saturation","volume":"45","author":"Jameson","year":"1955","journal-title":"JOSA"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1002\/(SICI)1520-6378(200002)25:1<49::AID-COL7>3.0.CO;2-4","article-title":"Testing CIELAB based Color difference Formulas","volume":"25","author":"Melgosa","year":"2000","journal-title":"Color Res. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"466","DOI":"10.4028\/www.scientific.net\/KEM.428-429.466","article-title":"Research on color space conversion model between XYZ and RGB","volume":"Volume 428","author":"Li","year":"2010","journal-title":"Key Engineering Materials"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Allaoui, M., Kherfi, M.L., and Cheriet, A. (2020). Considerably improving clustering algorithms using UMAP dimensionality reduction technique: A comparative study. International Conference on Image and Signal Processing, Springer.","DOI":"10.1007\/978-3-030-51935-3_34"},{"key":"ref_45","unstructured":"Brownlee, J. (2021, March 04). Auto-Sklearn for Automated Machine Learning in Python. Available online: https:\/\/machinelearningmastery.com\/auto-sklearn-for-automated-machine-learning-in-python\/."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Feurer, M., Klein, A., Eggensperger, K., Springenberg, J.T., Blum, M., and Hutter, F. (2019). Auto-sklearn: Efficient and robust automated machine learning. Automated Machine Learning, Springer.","DOI":"10.1007\/978-3-030-05318-5_6"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/TPAMI.2009.187","article-title":"Sensitivity analysis of k-fold cross validation in prediction error estimation","volume":"32","author":"Rodriguez","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1109\/TVCG.2020.3030361","article-title":"Pipelineprofiler: A visual analytics tool for the exploration of automl pipelines","volume":"27","author":"Ono","year":"2020","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Carvalho, D.V., Pereira, E.M., and Cardoso, J.S. (2019). Machine learning interpretability: A survey on methods and metrics. Electronics, 8.","DOI":"10.3390\/electronics8080832"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1175\/825.1","article-title":"The ROC curve and the area under it as performance measures","volume":"19","author":"Marzban","year":"2004","journal-title":"Weather Forecast."},{"key":"ref_52","unstructured":"Bonnin, R. (2017). Machine Learning for Developers: Uplift Your Regular Applications with the Power of Statistics, Analytics, and Machine Learning, Packt Publishing Ltd."},{"key":"ref_53","unstructured":"Bonaccorso, G. (2017). Machine Learning Algorithms, Packt Publishing Ltd."},{"key":"ref_54","unstructured":"Weiming, J.M. (2019). Mastering Python for Finance: Implement. Advanced State-of-the-Art Financial Statistical Applications Using Python, Packt Publishing Ltd."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Susmaga, R. (2004). Confusion matrix visualization. Intelligent Information Processing and Web Mining, Springer.","DOI":"10.1007\/978-3-540-39985-8_12"},{"key":"ref_56","unstructured":"Zdravevski, E., Lameski, P., and Kulakov, A. (2013, January 18\u201321). Advanced transformations for nominal and categorical data into numeric data in supervised learning problems. Proceedings of the 10th Conference for Informatics and Information Technology (CIIT), Bitola, North Macedonia."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Chang, C.-C., Lee, Y.-J., and Pao, H.-K. (2010, January 18\u201320). A Passive-aggressive algorithm for semi-supervised learning. Proceedings of the 2010 International Conference on Technologies and Applications of Artificial Intelligence, Hsinchu, Taiwan.","DOI":"10.1109\/TAAI.2010.61"},{"key":"ref_59","unstructured":"Brownlee, J. (2016). Machine Learning Mastery with Python: Understand Your Data, Create Accurate Models, and Work Projects End-to-End, Machine Learning Mastery."},{"key":"ref_60","first-page":"1","article-title":"Linear discriminant analysis-a brief tutorial","volume":"18","author":"Balakrishnama","year":"1998","journal-title":"Inst. Signal Inf. Process."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Taud, H., and Mas, J.F. (2018). Multilayer perceptron (MLP). Geomatic Approaches for Modeling Land Change Scenarios, Springer.","DOI":"10.1007\/978-3-319-60801-3_27"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Bottou, L. (2012). Stochastic gradient descent tricks. Neural Networks: Tricks of the Trade, Springer.","DOI":"10.1007\/978-3-642-35289-8_25"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1504\/IJAPR.2016.079050","article-title":"Linear vs. quadratic discriminant analysis classifier: A tutorial","volume":"3","author":"Tharwat","year":"2016","journal-title":"Int. J. Appl. Pattern Recognit."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3389\/fnbot.2013.00021","article-title":"Gradient boosting machines, a tutorial","volume":"7","author":"Natekin","year":"2013","journal-title":"Front. Neurorobotics"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Polikar, R. (2012). Ensemble learning. Ensemble Machine Learning, Springer.","DOI":"10.1007\/978-1-4419-9326-7_1"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Schapire, R.E. (2013). Explaining adaboost. Empirical Inference, Springer.","DOI":"10.1007\/978-3-642-41136-6_5"},{"key":"ref_68","first-page":"275","article-title":"An Introduction to Decision Tree Modeling","volume":"18","author":"Myles","year":"2004","journal-title":"J. Chemom. A J. Chemom. Soc."},{"key":"ref_69","first-page":"713","article-title":"Na\u00efve bayes","volume":"15","author":"Webb","year":"2010","journal-title":"Encycl. Mach. Learn."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/8\/4\/70\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:18:12Z","timestamp":1760167092000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/8\/4\/70"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,19]]},"references-count":69,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["informatics8040070"],"URL":"https:\/\/doi.org\/10.3390\/informatics8040070","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,19]]}}}