IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i10p1843-d1502099.html
   My bibliography  Save this article

Two-Stage Multimodal Method for Predicting Intramuscular Fat in Pigs

Author

Listed:
  • Wenzheng Liu

    (College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin 300392, China)

  • Tonghai Liu

    (College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300392, China)

  • Jianxun Zhang

    (College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China)

  • Fanzhen Wang

    (College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin 300392, China)

Abstract

Intramuscular fat (IMF) content significantly influences pork tenderness, flavor, and juiciness. Maintaining an optimal IMF range not only enhances nutritional value but also improves the taste of pork products. However, traditional IMF measurement methods are often invasive and time-consuming. Ultrasound imaging technology offers a non-destructive solution capable of predicting IMF content and assessing backfat thickness as well as longissimus dorsi muscle area size. A two-stage multimodal network model was developed in this study. First, using B-mode ultrasound images, we employed the UNetPlus segmentation network to accurately delineate the longissimus dorsi muscle area. Subsequently, we integrated data on backfat thickness and longissimus dorsi muscle area to create a multimodal input for IMF content prediction using our model. The results indicate that UNetPlus achieves a 94.17% mean Intersection over Union (mIoU) for precise longissimus dorsi muscle area segmentation. The multimodal network achieves an R 2 of 0.9503 for IMF content prediction, with Spearman and Pearson correlation coefficients of 0.9683 and 0.9756, respectively, all within a compact model size of 4.96 MB. This study underscores the efficacy of combining segmented longissimus dorsi muscle images with data on backfat thickness and muscle area in a two-stage multimodal approach for predicting IMF content.

Suggested Citation

  • Wenzheng Liu & Tonghai Liu & Jianxun Zhang & Fanzhen Wang, 2024. "Two-Stage Multimodal Method for Predicting Intramuscular Fat in Pigs," Agriculture, MDPI, vol. 14(10), pages 1-14, October.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:10:p:1843-:d:1502099
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/10/1843/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/14/10/1843/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:prg:jnlcfu:v:2022:y:2022:i:1:id:572 is not listed on IDEAS
    2. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    3. Frank, Johannes, 2023. "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics 03/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    4. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    5. Paulo Júlio & Pedro M. Esperança, 2012. "Evaluating the forecast quality of GDP components: An application to G7," GEE Papers 0047, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Apr 2012.
    6. Cameron Roach & Rob Hyndman & Souhaib Ben Taieb, 2021. "Non‐linear mixed‐effects models for time series forecasting of smart meter demand," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1118-1130, September.
    7. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    8. Nikitopoulos, Christina Sklibosios & Thomas, Alice Carole & Wang, Jianxin, 2023. "The economic impact of daily volatility persistence on energy markets," Journal of Commodity Markets, Elsevier, vol. 30(C).
    9. I. Yu. Zolotova & V. V. Dvorkin, 2017. "Short-term forecasting of prices for the Russian wholesale electricity market based on neural networks," Studies on Russian Economic Development, Springer, vol. 28(6), pages 608-615, November.
    10. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
    11. Thiyanga S. Talagala & Feng Li & Yanfei Kang, 2019. "Feature-based Forecast-Model Performance Prediction," Monash Econometrics and Business Statistics Working Papers 21/19, Monash University, Department of Econometrics and Business Statistics.
    12. Martin Guth, 2022. "Predicting Default Probabilities for Stress Tests: A Comparison of Models," Papers 2202.03110, arXiv.org.
    13. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    14. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    15. Wasilewski, J. & Baczynski, D., 2017. "Short-term electric energy production forecasting at wind power plants in pareto-optimality context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 177-187.
    16. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    17. Goldsworthy, M. & Moore, T. & Peristy, M. & Grimeland, M., 2022. "Cloud-based model-predictive-control of a battery storage system at a commercial site," Applied Energy, Elsevier, vol. 327(C).
    18. Bialowolski, Piotr & Kuszewski, Tomasz & Witkowski, Bartosz, 2015. "Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 9, pages 1-37.
    19. Kunze, Frederik, 2017. "Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts," University of Göttingen Working Papers in Economics 326, University of Goettingen, Department of Economics.
    20. Drachal, Krzysztof, 2021. "Forecasting crude oil real prices with averaging time-varying VAR models," Resources Policy, Elsevier, vol. 74(C).
    21. Banerjee, Nilabhra & Morton, Alec & Akartunalı, Kerem, 2020. "Passenger demand forecasting in scheduled transportation," European Journal of Operational Research, Elsevier, vol. 286(3), pages 797-810.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:14:y:2024:i:10:p:1843-:d:1502099. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.