# Line Laser Scanning Combined with Machine Learning for Fish Head Cutting Position Identification

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Materials

#### 2.2. Data Acquisition and Pre-Processing

#### 2.2.1. Data Acquisition System

#### 2.2.2. Data Acquisition Process

#### 2.2.3. Data Validity Discernment

#### 2.2.4. Data Filtering

#### 2.3. Fish Head Cut Position Identification

#### 2.3.1. Feature Extraction

_{ij}represents the standardized matrix combined by column, D represents the correlation coefficient matrix, λ represents the eigenvalues of this matrix, P

_{I}represents the component matrix, U

_{I}represents the index coefficient matrix, and F

_{i}represents the principal component values of the ventral–dorsal divide of the fish.

#### 2.3.2. Establishment of Fish Head Cut Position Identification Models

#### LS-SVM Model

#### PSO-BP Model

#### LSTM Model

#### 2.4. Model Evaluation Metrics

## 3. Results

#### 3.1. Data Pre-Processing

#### 3.1.1. Data Segmentation

#### 3.1.2. Data Filtering

#### 3.2. Fish Head Cut Position Identification

#### 3.2.1. Extraction of Ventral–Dorsal Demarcation Line

#### 3.2.2. Data Dimensionality Reduction of Abdominal and Dorsal Dividing Lines

#### 3.2.3. Fish Head Cutting Position Identification Model

#### LS-SVM Model

#### PSO-BP Model

#### LSTM Model

## 4. Conclusions

^{2}and RPD of 0.9480 and 3.1426, and lowest RMSE and MAE of 0.2957 and 0.1933, respectively, in the test set. These results validate the potential of combining line laser scanning techniques with machine learning for fish head cutting position identification. In future studies, experiments will be conducted on more types and sizes of fish to improve the reliability and practicality of the model.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Azarmdel, H.; Mohtasebi, S.S.; Jafari, A.; Muñoz, A.R. Developing an orientation and cutting point determination algorithm for a trout fish processing system using machine vision. Comput. Electron. Agric.
**2019**, 162, 613–629. [Google Scholar] [CrossRef] - Qing, C.; Branch, D. Method study on mechanized head cutting for marine small fish. Fish. Mod.
**2012**, 39, 38–42. [Google Scholar] - Wang, H.H.; Zhang, X.Y.; Li, P.P.; Sun, J.L.; Yan, P.T.; Zhang, X.; Liu, Y.Q. A New Approach for Unqualified Salted Sea Cucumber Identification: Integration of Image Texture and Machine Learning under the Pressure Contact. J. Sens.
**2020**, 2020, 8834614. [Google Scholar] [CrossRef] - Mustafa, A.; Volino, M.; Kim, H.; Guillemaut, J.-Y.; Hilton, A. Temporally Coherent General Dynamic Scene Reconstruction. Int. J. Comput. Vis.
**2021**, 129, 123–141. [Google Scholar] [CrossRef] - Wang, J.H.; Yang, Y.X.; Zhou, Y.G. Dynamic Three-Dimensional Surface Reconstruction Approach for Continuously Deformed Objects. IEEE Photonics J.
**2021**, 13, 1–15. [Google Scholar] [CrossRef] - Liu, Z.; Xiang, C.Q.; Chen, T. Automated Binocular Vision Measurement of Food Dimensions and Volume for Dietary Evaluation. Comput. Sci. Eng.
**2018**. Available online: https://ieeexplore.ieee.org/abstract/document/8365083 (accessed on 13 December 2023). - Tsoulias, N.; Xanthopoulos, G.; Fountas, S.; Zude, M. In-situ detection of apple fruit using a 2D LiDAR laser scanner. In Proceedings of the IEEE International Workshop on Metrology for Agriculture and Forestry, Trento, Italy, 4–6 November 2020. [Google Scholar]
- Antequera, T.; Caballero, D.; Grassi, S.; Uttaro, B.; Perez-Palacios, T. Evaluation of fresh meat quality by Hyperspectral Imaging (HSI), Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI): A review. Meat Sci.
**2020**, 172, 108340. [Google Scholar] [CrossRef] [PubMed] - Ando, M.; Sugiyama, H.; Maksimenko, A.; Rubenstein, E.; Roberson, J.; Shimao, D.; Hashimoto, E.; Mori, K. X-ray dark-field imaging and its application to medicine. Radiat. Phys. Chem.
**2004**, 71, 899–904. [Google Scholar] [CrossRef] - Weisgerber, J.N.; Medill, S.A.; McLoughlin, P.D. Parallel-Laser Photogrammetry to Estimate Body Size in Free-Ranging Mammals. Wildl. Soc. Bull.
**2015**, 39, 422–428. [Google Scholar] [CrossRef] - Chen, H.; Liu, Z.; Gu, J.; Ai, W.; Wen, J.; Cai, K. Quantitative analysis of soil nutrition based on FT-NIR spectroscopy integrated with BP neural deep learning. Anal. Methods
**2018**, 10, 5004–5013. [Google Scholar] [CrossRef] - Cobourn, W.G.; Dolcine, L.; French, M.; Hubbard, M.C. A comparison of nonlinear regression and neural network models for ground-level ozone forecasting. Air Repair
**2000**, 50, 1999–2009. [Google Scholar] [CrossRef] [PubMed] - Lin, W.M.; Tu, C.S.; Yang, R.F.; Tsai, M.T. Particle swarm optimisation aided least-square support vector machine for load forecast with spikes. Iet Gener. Transm. Distrib.
**2016**, 10, 1145–1153. [Google Scholar] [CrossRef] - Zhu, F.L.; He, Y.; Shao, Y.N. Application of Near-Infrared Hyperspectral Imaging to Predicting Water Content in Salmon Flesh. Spectrosc. Spectr. Anal.
**2015**, 35, 113–117. [Google Scholar] - Zhang, Y.; Jia, S.; Zhang, W. Predicting acetic acid content in the final beer using neural networks and support vector machine. J. Inst. Brew.
**2012**, 118, 361–367. [Google Scholar] [CrossRef] - Khoshnoudi-Nia, S.; Moosavi-Nasab, M. Nondestructive Determination of Microbial, Biochemical, and Chemical Changes in Rainbow Trout (Oncorhynchus mykiss) During Refrigerated Storage Using Hyperspectral Imaging Technique. Food Anal. Methods
**2019**, 12, 1635–1647. [Google Scholar] [CrossRef] - Wang, F.R.; Xie, B.M.; Lue, E.L.; Zeng, Z.X.; Mei, S.; Ma, C.Y.; Guo, J.M. Design of a Moisture Content Detection System for Yinghong No. 9 Tea Leaves Based on Machine Vision. Appl. Sci.
**2023**, 13, 1806. [Google Scholar] [CrossRef] - Deng, Y.; Xiao, H.J.; Xu, J.X.; Wang, H. Prediction model of PSO-BP neural network on coliform amount in special food. Saudi J. Biol. Sci.
**2019**, 26, 1154–1160. [Google Scholar] [CrossRef] [PubMed] - Zhang, L.; Liu, J.; Zhi, L. Research on Grain Yield Prediction Method Based on Improved PSO-BP. J. Appl. Sci.
**2014**, 14, 1990–1995. [Google Scholar] [CrossRef] - Örnek, M.N.; Örnek, H.K. Developing a deep neural network model for predicting carrots volume. J. Food Meas. Charact.
**2021**, 15, 3471–3479. [Google Scholar] [CrossRef] - Zhong, N.; Li, Y.P.; Li, X.Z.; Guo, C.X.; Wu, T. Accurate prediction of salmon storage time using improved Raman spectroscopy. J. Food Eng.
**2021**, 293, 110378. [Google Scholar] [CrossRef] - Gao, C.; Bao, S.; Zhou, C.; He, X.; Shiju, E.; Sun, J.; Gong, J. Research on the line point cloud processing method for railway wheel profile with a laser profile sensor. Measurement
**2023**, 211, 112640. [Google Scholar] [CrossRef] - Zabalza, J.; Ren, J.; Ren, J.; Liu, Z.; Marshall, S. Structured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging. Appl. Opt.
**2014**, 53, 4440–4449. [Google Scholar] [CrossRef] [PubMed] - Zhang, X.L.; Liu, F.; He, Y.; Li, X.L. Application of Hyperspectral Imaging and Chemometric Calibrations for Variety Discrimination of Maize Seeds. Sensors
**2012**, 12, 17234–17246. [Google Scholar] [CrossRef] [PubMed] - Tarhouni, M.; Laabidi, K.; Lahmari-Ksouri, M.; Zidi, S. System Identification based on Multi-kernel Least Squares Support Vector Machines (Multi-kernel LS-SVM). In Proceedings of the International Joint Conference on Computational Intelligence, Dhaka, Bangladesh, 14–15 December 2018. [Google Scholar]
- Zhang, X.; Sun, J.L.; Li, P.P.; Zeng, F.Y.; Wang, H.H. Hyperspectral detection of salted sea cucumber adulteration using different spectral preprocessing techniques and SVM method. Lwt-Food Sci. Technol.
**2021**, 152, 112295. [Google Scholar] [CrossRef] - Wang, J.; Si, H.P.; Gao, Z.; Shi, L. Winter Wheat Yield Prediction Using an LSTM Model from MODIS LAI Products. Agriculture
**2022**, 12, 1707. [Google Scholar] [CrossRef] - Qi, J.; Du, J.; Siniscalchi, S.M.; Ma, X.L.; Lee, C.H. Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network-Based Vector-to-Vector Regression. IEEE Trans. Signal Process.
**2020**, 68, 3411–3422. [Google Scholar] [CrossRef] - Weijie, W.; Yanmin, L. Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model. In OP Conference Series: Materials Science and Engineering; IOP Publishing: Bristol, UK, 2018; p. 012049. [Google Scholar]
- Cheng, J.H.; Sun, D.W.; Pu, H.B.; Zhu, Z.W. Development of hyperspectral imaging coupled with chemometric analysis to monitor K value for evaluation of chemical spoilage in fish fillets. Food Chem.
**2015**, 185, 245–253. [Google Scholar] [CrossRef] - Khoshnoudi-Nia, S.; Moosavi-Nasab, M. Prediction of various freshness indicators in fish fillets by one multispectral imaging system. Sci. Rep.
**2019**, 9, 14704. [Google Scholar] [CrossRef]

**Figure 1.**Relationship between head cutting position and fish body parameters: (a) fish head length; (b) total length; (c) maximum width; (d) maximum thickness; (e) ideal cutting line; (f) gill.

**Figure 2.**Fish head cutting position determination flow chart: (

**a**) sample; (

**b**) data acquisition; (

**c**) data pre-processing; (

**d**) feature extraction; the different colored lines represent the ventral–dorsal dividing line extracted from different samples; (

**e**) model building; (

**f**) evaluation; (

**g**) fish head removal.

**Figure 3.**Data acquisition process: (

**a**) coordinate diagram; (

**b**) scanning process diagram; (

**c**) radial section contour diagram; (

**d**) Y projection contour diagram.

**Figure 7.**Upper surface data collection of fish: (

**a**) laser point cloud data collection; (

**b**) fish surface data after segmentation. The different colored lines represent the results of a single line laser scan and consist of multiple points that form a radial profile.

**Figure 8.**Data filtering: (

**a**) original data; (

**b**) Kalman-filtered data; (

**c**) median-filtered data; (

**d**) Kalman- and median-filtered data.

**Figure 9.**Abdominal and dorsal dividing lines. The different colored lines represent the ventral–dorsal dividing line extracted from different samples.

Sample Size | Statistical Indicator | a/mm | b/mm | c/mm | d/mm | Weight/g |
---|---|---|---|---|---|---|

204 | Maximum value | 63.1 | 239.4 | 103.7 | 51.54 | 569 |

Minimum value | 50.1 | 200.8 | 94.6 | 45.3 | 473 | |

Mean | 223.6 | 54.6 | 100.9 | 47.49 | 532.1 | |

Standard deviation | 2.5 | 8.9 | 2.2 | 1.85 | 21.6 |

Component | Initial Eigenvalue | Extraction of Squares and Loading | Rotate the Square and Load | ||||||
---|---|---|---|---|---|---|---|---|---|

Total | Variance of % | Cumulative % | Total | Variance of % | Cumulative % | Total | Variance of % | Cumulative % | |

1 | 43.031 | 71.718 | 71.718 | 43.031 | 71.718 | 71.718 | 34.740 | 57.901 | 57.901 |

2 | 11.990 | 19.983 | 91.702 | 11.990 | 19.983 | 91.702 | 16.894 | 28.157 | 86.058 |

3 | 1.999 | 3.3319 | 95.033 | 1.999 | 3.331 | 95.033 | 5.385 | 8.974 | 95.033 |

4 | 0.842 | 1.403 | 96.436 | ||||||

5 | 0.386 | 0.643 | 97.079 | ||||||

6 | 0.340 | 0.567 | 97.646 | ||||||

7 | 0.261 | 0.434 | 98.080 | ||||||

8 | 0.139 | 0.232 | 98.312 | ||||||

9 | 0.112 | 0.187 | 98.496 | ||||||

10 | 0.097 | 0.161 | 98.661 | ||||||

...... | |||||||||

204 | −1.04 × 10^{−15} | −1.74 × 10^{−15} | 100.00 |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Zhang, X.; Gong, Z.; Liang, X.; Sun, W.; Ma, J.; Wang, H.
Line Laser Scanning Combined with Machine Learning for Fish Head Cutting Position Identification. *Foods* **2023**, *12*, 4518.
https://doi.org/10.3390/foods12244518

**AMA Style**

Zhang X, Gong Z, Liang X, Sun W, Ma J, Wang H.
Line Laser Scanning Combined with Machine Learning for Fish Head Cutting Position Identification. *Foods*. 2023; 12(24):4518.
https://doi.org/10.3390/foods12244518

**Chicago/Turabian Style**

Zhang, Xu, Ze Gong, Xinyu Liang, Weichen Sun, Junxiao Ma, and Huihui Wang.
2023. "Line Laser Scanning Combined with Machine Learning for Fish Head Cutting Position Identification" *Foods* 12, no. 24: 4518.
https://doi.org/10.3390/foods12244518