Computer Vision and Machine Learning for Tuna and Salmon Meat Classification

Medeiros, Erika Carlos; Almeida, Leandro; Filho, José Gilson de Almeida Teixeira

doi:10.3390/informatics8040070

Cited by 6 publications

(3 citation statements)

References 54 publications

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“…Their proposed DNN classifier achieved the optimum performance of 90.0%. Medeiros et al [7] presented a solution for classifying the freshness levels of tuna and salmon. This study generated datasets from the RGB, HSV, HSI, and L*, a*, b* spaces of the collected images, and used machine learning classification evaluation metrics of accuracy, receiver operating characteristic curve, precision, recall, f1-score, and confusion matrix to emphasize a nondestructive method for external quality detection of tuna and salmon meat products.…”

Section: Related Workmentioning

confidence: 99%

Color Change Analysis of Raw Chicken Breast over Different Timestamps Using CNNs

Zou,

Kageyama,

Yuan

2024

IEEJ Transactions Elec Engng

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In this study, the potential of a novel method based on an artificial neural network was investigated to analyze the color change of raw chicken breasts during refrigeration. As edible meat for people, chicken breasts have high nutrients and low‐fat content. Therefore, people consume it as a safe and high‐value food in their daily diet. The investigation of chicken breast freshness is proposed as a significant issue in the meat industry because raw meat spills rapidly. The color change of raw chicken breasts over time reflects subtle biochemical changes, as well as changes in freshness. However, owing to the impact of the photographic equipment and illumination, a significant color discrepancy exists between the captured image and the intrinsic color. It is difficult to separate chicken breast color changes from color discrepancies when comparing images captured at different timestamps. Thus, we propose a color change analysis method for raw chicken breast that uses color correction to suppress the influence of color discrepancy and uses CNN to extract discriminant features for timestamp classification analysis. The experimental results indicate that the proposed method improves all three CNN models by achieving higher accuracy, and the best model was improved from to , demonstrating the effectiveness of the proposed method. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Section: Related Workmentioning

confidence: 99%

Color Change Analysis of Raw Chicken Breast over Different Timestamps Using CNNs

Zou,

Kageyama,

Yuan

2024

IEEJ Transactions Elec Engng

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“…In computer science, ML is used for various tasks, such as natural language processing [1][2][3][4][5], image recognition [6][7][8], or computer vision [9][10][11][12][13][14]. In engineering, ML is applied in the optimization and control of complex systems [15][16][17][18], the prediction of equipment failures [19][20][21], or the enhancement of manufacturing processes [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

X-ray Diffraction Data Analysis by Machine Learning Methods—A Review

Surdu,

Győrgy

2023

Applied Sciences

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X-ray diffraction (XRD) is a proven, powerful technique for determining the phase composition, structure, and microstructural features of crystalline materials. The use of machine learning (ML) techniques applied to crystalline materials research has increased significantly over the last decade. This review presents a survey of the scientific literature on applications of ML to XRD data analysis. Publications suitable for inclusion in this review were identified using the “machine learning X-ray diffraction” search term, keeping only English-language publications in which ML was employed to analyze XRD data specifically. The selected publications covered a wide range of applications, including XRD classification and phase identification, lattice and quantitative phase analyses, and detection of defects and substituents, as well as microstructural material characterization. Current trends in the field suggest that future efforts pertaining to the application of ML techniques to XRD data analysis will address shortcomings of ML approaches related to data quality and availability, interpretability of the results and model generalizability and robustness. Additionally, future research will likely incorporate more domain knowledge and physical constraints, integrate with quantum physical methods, and apply techniques like real-time data analysis and high-throughput screening to accelerate the discovery of tailored novel materials.

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“…Among these models, RF, GBDT, and KNN were top ranked as the most accurate learning algorithms. RF could reduce the variance in decision trees and therefore improve the accuracy and also could be used in large‐scale datasets (Chen et al, 2013); GBDT has the high prediction accuracy, with the flexibility to deal with various types of data with good performances (Friedman, 2002); one of the most significant advantages of KNN algorithm is that there is no need to tune several parameters, and it can be used for classification problems with its high prediction accuracy (Medeiros et al, 2021); compared with the above‐mentioned models, some other models, such as LR, do not require very complicated calculations and run fast when the amount of data is large with low operation costs (Pan, 1994). Nevertheless, using one single machine learning model might lead to overfitting performance and reduced generalization ability of the model, which means that when the model has been trained too well on training data, it may make inaccurate predictions when adapted to new and previously unseen data (Rong et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Comparison of machine learning models within different spatial resolutions for predicting the bigeye tuna fishing grounds in tropical waters of the Atlantic Ocean

Song

Zhang

et al. 2023

Fisheries Oceanography

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To understand the effects of the machine learning models and the spatial resolutions on the prediction accuracy of bigeye tuna (Thunnus obesus) fishing grounds, logbook data of 13 Chinese longliners operating in the high seas of the Atlantic Ocean from 2016 to 2019 were collected. The environmental factors were selected based on the correlation analysis of calculation of catch per unit effort (CPUE) and the marine vertical environmental factors. Five machine learning models: random forest, gradient‐boosting decision tree, K‐nearest neighbor, logistic regression and stacking ensemble learning (STK) within four spatial resolutions of .5° × .5°, 1° × 1°, 2° × 2° and 5° × 5° grids were constructed and compared. Results showed that (1) the prediction performance of STK model was the best, with the highest scores of the four evaluation indexes, accuracy (Acc), precision (P), recall (R), and F1‐score (F1), and the highest correct prediction rate for predicting “high CPUE fishing ground”; (2) models within the spatial resolution of 1° × 1° grids predicted the better results compared with .5° × .5°, 2° × 2° and 5° × 5° grids; (3) the vertical environmental factors selected based on the correlation analysis could be used as reliable predictors in the models. Results suggested that using STK within 1° × 1° grids could improve the generalization performance and prediction accuracy for predicting the bigeye tuna fishing grounds in the Atlantic Ocean.

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Computer Vision and Machine Learning for Tuna and Salmon Meat Classification

Cited by 6 publications

References 54 publications

Color Change Analysis of Raw Chicken Breast over Different Timestamps Using CNNs

Color Change Analysis of Raw Chicken Breast over Different Timestamps Using CNNs

X-ray Diffraction Data Analysis by Machine Learning Methods—A Review

Comparison of machine learning models within different spatial resolutions for predicting the bigeye tuna fishing grounds in tropical waters of the Atlantic Ocean

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