BEGV2-UNet: A method for automatic segmentation and calculation of backfat and eye muscle region in pigs

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture Pub Date : 2025-03-12 DOI:10.1016/j.compag.2025.110272

Wenzheng Liu , Tonghai Liu , Jinghan Cai , Zhihan Li , Xue Wang , Rui Zhang , Xiaoyue Seng

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引用次数: 0

Abstract

Rapid and accurate measurements of eye muscle area and backfat thickness in breeding pigs is crucial for improving breeding traits. Within reasonable ranges, these traits significantly influence the number of piglets born, their birth weights, and survival rates. Traditional detection methods are time-consuming and heavily reliant on operational expertise. While B-mode ultrasound is widely used as a non-invasive tool for measuring backfat thickness and eye muscle area, its efficiency and precision are limited by dependence on the operator.

To address these issues, this study introduces the BEGV2-UNet model, an innovative UNet network based on reconstructing down-sampling and up-sampling paths, incorporating GhostModuleV2, and incorporating a large kernel attention mechanism to better capture the boundaries and positions of backfat and eye muscle regions. The model can be used to segment these regions in breeding pigs and improve the loss function for accelerate convergence while remedying the low precision caused by class imbalance. Using a dataset of ultrasound images, the BEGV2-UNet model achieved an MIoU of 96.18 % and MPA of 98.12 %, with model size reduced to 18.69 MB and strong inference accuracy. We calculated the backfat thickness and eye muscle area using the model to achieve R² values of 0.98 and 0.96, respectively.

This study highlights the significant advantages of BEGV2-UNet in terms of image segmentation accuracy and lightweight design.

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来源期刊

Computers and Electronics in Agriculture 工程技术-计算机：跨学科应用

CiteScore

15.30

自引率

14.50%

发文量

800

审稿时长

62 days

期刊介绍： Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.