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CAT-CBAM-Net:一种基于CNN和变压器的自动评分方法,用于测量体状况

Hongxiang Xue^1,2, Yuwen Sun^1,2, Jinxin Chen^1,2

¹College of Engineering, Nanjing Agricultural University, Nanjing 210031, China.

Sensors (Basel, Switzerland)

|September 28, 2023

概括

这项研究引入了一种使用双神经网络的自动母猪身体状况评分系统. 人工智能模型准确评估母猪的健康状况,提高生殖性能和农场管理效率.

科学领域:

动物科学动物科学
计算机科学计算机科学
人工智能的人工智能

背景情况:

体状况评分对于优化猪养殖中的营养和生殖性能至关重要.
手动评估方法是劳动密集型和耗时的,特别是在大规模操作中.
准确的身体状况评分直接影响种子的健康和生产力.

研究的目的:

利用人工智能开发一种用于母猪体质评分的自动化系统.
提高商业养猪场母猪体状况评估的效率和准确性.
增强在母猪图像中捕捉本地和全球特征,以便更好地分析.

主要方法:

采用了结合卷积神经网络 (CNN) 和变压器网络的双神经网络架构.
集成了一个频道注意模块 (CBAM),以专注于相关的图像特征.
使用优化的焦点损失函数来解决数据不平衡和错误标签问题.

主要成果:

开发的方法实现了高性能指标:91.06%的平均精度,91.58%的平均回忆率和91.31%的平均F1分数.
对比实验证实了拟议方法在数据集上的卓越性能.
该系统证明了母猪身体状况的有效自动评分.

结论:

这种由人工智能驱动的系统比手动的母猪身体状况得分提供了显著的进步.
这项技术有望用于提高猪管理的效率和准确性.
自动评分可以导致更明智的营养决策和提高母猪生殖结果.

关键词:

人工神经网络的人工神经网络身体状况得分身体状况得分机器视觉机器视觉机器视觉有分娩的母猪.

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