Next Article in Journal
Revising the Classic Computing Paradigm and Its Technological Implementations
Previous Article in Journal
Arabic Offensive and Hate Speech Detection Using a Cross-Corpora Multi-Task Learning Model
Article

Computer Vision and Machine Learning for Tuna and Salmon Meat Classification

Centro de Informática, Universidade Federal de Pernambuco, University City, Recife 50.740-560, Brazil
*
Author to whom correspondence should be addressed.
Academic Editors: Phuong T. Nguyen and Vito Walter Anelli
Received: 13 August 2021 / Revised: 9 September 2021 / Accepted: 20 September 2021 / Published: 19 October 2021
(This article belongs to the Special Issue Applications of Machine Learning and Deep Learning in Agriculture)
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’ high accuracy. View Full-Text
Keywords: computer vision; machine learning; tuna meat freshness; salmon meat freshness computer vision; machine learning; tuna meat freshness; salmon meat freshness
Show Figures

Figure 1

MDPI and ACS Style

Medeiros, E.C.; Almeida, L.M.; Filho, J.G.d.A.T. Computer Vision and Machine Learning for Tuna and Salmon Meat Classification. Informatics 2021, 8, 70. https://0-doi-org.brum.beds.ac.uk/10.3390/informatics8040070

AMA Style

Medeiros EC, Almeida LM, Filho JGdAT. Computer Vision and Machine Learning for Tuna and Salmon Meat Classification. Informatics. 2021; 8(4):70. https://0-doi-org.brum.beds.ac.uk/10.3390/informatics8040070

Chicago/Turabian Style

Medeiros, Erika C., Leandro M. Almeida, and José G.d.A.T. Filho 2021. "Computer Vision and Machine Learning for Tuna and Salmon Meat Classification" Informatics 8, no. 4: 70. https://0-doi-org.brum.beds.ac.uk/10.3390/informatics8040070

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop