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用3D点云和基于遗传算法的波形神经网络来预测家禽尸体内脏尺寸的方法.

Zhengwei Zhu1, Yan Chen1, Lu Cai1

  • 1College of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei, 430048, China.

Poultry science
|December 4, 2024
PubMed
概括

本研究介绍了一个3D点云和基因算法-波纹神经网络 (GA-WNN) 模型,用于预测家禽内脏尺寸. 该GA-WNN模型准确地预测了内脏大小,有助于无损的自动化内脏切除.

关键词:
3D点云是一个3D点云.基于遗传算法的波形神经网络.平均绝对百分比错误的平均值.禽类内脏是什么意思根的平均平方误差.

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科学领域:

  • 农业工程 农业工程
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 禽类内脏剥离可能会损害内部器官,降低产品价值.
  • 准确预测内脏尺寸对于自动化处理至关重要.

研究的目的:

  • 开发一个预测模型,用于家禽尸体内脏尺寸.
  • 通过防止内脏损伤来增强自动化内脏剥离技术.

主要方法:

  • 利用3D点云扫描和逆向工程软件收集家禽尸体数据.
  • 开发了一个基于遗传算法的波形神经网络 (GA-WNN) 模型.
  • 使用相关性分析来确定输入目标和K折交叉验证.

主要成果:

  • 与其他六种模型相比,GA-WNN模型显示出更高的预测准确性.
  • 对于大多数内在维度预测,实现了最低的平均绝对百分比误差 (MAPE) 和根平均平方误差 (RMSE).
  • 验证了模型在不同家禽品种的通用性.

结论:

  • 3D点云和GA-WNN模型准确地预测了家禽的内脏尺寸.
  • 这种方法为开发无害的自动化内脏清除系统提供了理论基础.
  • 该方法在预测内脏位置方面提供了显著的优势,改善了处理结果.