Research on fresh image recognition algorithms based on machine learning
DOI:
https://doi.org/10.56294/sctconf2024.698Keywords:
Type and freshness identification (TFI), ML (machine learning), meats, pre-processing, feature extractionAbstract
The identification of fresh images tackles issues related to accurate classification, speed and flexibility enhancement, and perhaps superior food safety evaluation. In this work, the type and freshness identification (TFI) system is based on ML (machine learning). The research suggests ML techniques for identifying various meats (pork, chicken, beef, etc) flaws and differentiating between fresh and decomposing meats to decrease labour expenses, manufacturing time, and worker effort. An efficient TFI system is suggested in this work using machine learning (ML) techniques. We gather various meat samples to effectively identify the type and freshness of the meat. Pre-processing of raw images is conducted to standardize the raw data samples. In the feature extraction process, features from the normalized data are extracted to confirm the quality of the data. The retrieved data is divided into categories for fresh meat and non-fresh meat. The suggested approach is used to evaluate TFI efficiency using a Python program. In conclusion, it was discovered that this study outperformed in improving the TFI performance
References
[1] Fang H, Li S, Jiang W, Zhao H, Han C, Li J, Wang G, Zhang Y, Wang S, Deng J, Feng B. Ppb-level H2S gas sensor based on CuNi-MOFs derivatives for meat freshness detection at low temperature environment. Sensors and Actuators B: Chemical. 2022 Oct 1;368:132225. https://doi.org/10.1016/j.snb.2022.132225.
[2]Weng X, Luan X, Kong C, Chang Z, Li Y, Zhang S, Al-Majeed S, Xiao Y. A comprehensive method for assessing meat freshness using fusing electronic nose, computer vision, and artificial tactile technologies. Journal of Sensors. 2020;2020(1):8838535.https://doi.org/10.1155/2020/8838535.
[3]Lin Y, Ma J, Cheng JH, Sun DW. Visible detection of chilled beef freshness using a paper-based colourimetric sensor array combining with deep learning algorithms. Food Chemistry. 2024 May 30;441:138344.https://doi.org/10.1016/j.foodchem.2023.138344.
[4]Xiong, Y., Li, Y., Wang, C., Shi, H., Wang, S., Yong, C., Gong, Y., Zhang, W. and Zou, X., 2023. Non-destructive detection of chicken freshness based on electronic nose technology and transfer learning. Agriculture, 13(2), p.496.https://doi.org/10.3390/agriculture13020496.
[5]Liu T, Zhang W, Yuwono M, Zhang M, Ueland M, Forbes SL, Su SW. A data-driven meat freshness monitoring and evaluation method using rapid centroid estimation and hidden Markov models. Sensors and Actuators B: Chemical. 2020 May 15;311:127868.https://doi.org/10.1016/j.snb.2020.127868
[6]Lin Y, Ma J, Sun DW, Cheng JH, Zhou C. Fast real-time monitoring of meat freshness based on fluorescent sensing array and deep learning: From development to deployment. Food chemistry. 2024 Aug 1;448:139078.https://doi.org/10.1016/j.foodchem.2024.139078.
[7]Liu C, Chu Z, Weng S, Zhu G, Han K, Zhang Z, Huang L, Zhu Z, Zheng S. Fusion of electronic nose and hyperspectral imaging for mutton freshness detection using input-modified convolution neural network. Food Chemistry. 2022 Aug 15;385:132651.https://doi.org/10.1016/j.foodchem.2022.132651.
[8]Shin S, Lee Y, Kim S, Choi S, Kim JG, Lee K. Rapid and non-destructive spectroscopic method for classifying beef freshness using a deep spectral network fused with myoglobin information. Food Chemistry. 2021 Aug 1;352:129329.https://doi.org/10.1016/j.foodchem.2021.129329.
[9]Rayan MA, Rahim A, Rahman MA, Marjan MA, Ali UM. Fish freshness classification using combined deep learning model. In2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI) 2021 Jul 8 (pp. 1-5). IEEE.https://doi.org/10.1109/ACMI53878.2021.9528138.
[10]Ren X, Wang Y, Huang Y, Mustafa M, Sun D, Xue F, Chen D, Xu L, Wu F. A CNN-based E-nose using time series features for food freshness classification. IEEE Sensors Journal. 2023 Feb 13;23(6):6027-38.https://doi.org/10.1109/JSEN.2023.3241842.
[11]Sabilla SI, Sarno R, Triyana K, Hayashi K. Deep learning in a sensor array systembasedonthedistributionofvolatilecompoundsfrommeatcutsusing GC–MS analysis. Sensing and Bio-SensingResearch. 2020 Aug1;29:100371.https://doi.org/10.1016/j.sbsr.2020.100371.
[12]Sun Y, Zhang M, Adhikari B, Devahastin S, Wang H. Double-layer indicator films aided by BP-ANN-enabled freshness detection on packaged meat products. Food Packaging and Shelf Life. 2022 Mar 1;31:100808.https://doi.org/10.1016/j.fpsl.2021.100808.
[13]Sonwani E, Bansal U, Alroobaea R, Baqasah AM, Hedabou M. An artificial intelligence approach toward food spoilage detection and analysis. Frontiers in Public Health. 2022 Jan 12;9:816226.https://doi.org/10.3389/fpubh.2021.816226.
[14]Guo W, Li X, Xie T. Method and system for nondestructive detection of freshness in Penaeus vannamei based on hyperspectral technology. Aquaculture. 2021 May 30;538:736512.https://doi.org/10.1016/j.aquaculture.2021.736512.
[15]Chen Z, Wang Q, Zhang H, Nie P. Hyperspectral imaging (HSI) technology for the non-destructive freshness assessment of pearl gentian grouper under different storage conditions. Sensors. 2021 Jan 15;21(2):583.https://doi.org/10.3390/s21020583.
[16]Wang C, Lv C, Li R, Wang P, Wang X, Zhao A, Jin S, Han B, Lu S. Design and implementation of pork freshness grading based on deep learning. In2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) 2021 Nov 5 (pp. 216-220). https://doi.org/IEEE.10.1109/ICAICE54393.2021.00050.
[17]Putra GB, Prakasa E. Classification of chicken meat freshness using convolutional neural network algorithms. In2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT) 2020 Dec 20 (pp. 1-6). IEEE.https://doi.org/10.1109/3ICT51146.2020.9312018.
[18]Valentino F, Cenggoro TW, Pardamean B. A design of deep learning experimentation for fruit freshness detection. InIOP conference series: earth and environmental science 2021 Jul 1 (Vol. 794, No. 1, p. 012110). IOP Publishing.https://doi.org/10.1088/1755-1315/794/1/012110.
[19]Zou L, Liu W, Lei M, Yu X. An improved residual network for pork freshness detection using near-infrared spectroscopy. Entropy. 2021 Sep 30;23(10):1293.https://doi.org/10.3390/e23101293.
[20]Huang C, Gu Y. A machine learning method for the quantitative detection of adulterated meat using a MOS-based E-nose. Foods. 2022 Feb 20;11(4):602.https://doi.org/10.3390/foods11040602.
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Copyright (c) 2024 Rong Zhang , Jeffrey Sarmiento, Anton Louise De Ocampo, Rowell Hernandez (Author)
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