Two-Stage Multimodal Method for Predicting Intramuscular Fat in Pigs

Wenzheng Liu, Tonghai Liu (), Jianxun Zhang and Fanzhen Wang
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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

Agriculture, 2024, vol. 14, issue 10, 1-14

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.

Keywords: deep learning; pig; multimodal; intramuscular fat (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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