双流融合网络与ConvNeXtV2用于使用RGB-D数据在通道中的猪体重估计
Zujie Tan1,2, Junbin Liu1,2, Deqin Xiao1,2
1College of Mathematics Informatics, South China Agricultural University, Guangzhou 510642, China.
Animals : an open access journal from MDPI
|December 23, 2023
概括
在没有身体接触的情况下准确估计猪的体重对于畜牧管理至关重要. 我们的新深度视觉算法MPWEADV使用RGB和深度图像精确测量移动的猪,改进育种计划.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 动物科学动物科学
背景情况:
- 不接触猪的体重估计对于畜牧管理至关重要.
- 现实世界农业环境带来了诸如可变照明和动物运动等挑战.
- 现有的方法在动态环境中难以准确.
研究的目的:
- 开发一种创新的算法,用于准确的,非接触式的移动猪体重估计.
- 在可变的农业条件下克服当前方法的局限性.
- 为实时猪状况评估提供一个工具.
主要方法:
- 开发了基于深度视觉 (MPWEADV) 的移动猪体重估计算法.
- 利用RGB和深度图像通过ConvNeXtV2网络进行特征提取.
- 集成了功能融合模块与可信度图估计器,用于多模式数据集成.
主要成果:
- 取得了4.082公斤的平方根平均误差 (RMSE).
- 在测试组件上达到2.383%的平均绝对百分比误差 (MAPE).
- 与复制最新研究的模型相比,表现出卓越的性能.
结论:
- 该MPWEADV算法提供准确和实时的非接触猪体重估计.
- 这项技术支持在畜牧管理中改进分类和育种计划调整.
- 该方法显示出在农业环境中不受限制的应用的巨大潜力.
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