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通过深度学习和机器学习开发用于异常分散和运动的自动预警系统.

Bo-Lin Chen1, Ting-Hui Cheng1, Yi-Che Huang1

  • 1Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan.

Poultry science
|September 28, 2023
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概括
此摘要是机器生成的。

这项研究开发了一种自动化系统来监测群的分散和移动,改善了农场健康监测. 该系统使用人工智能检测异常,节省劳动力和减少疾病传播风险.

关键词:
卷积神经网络 (CNN) 是一种神经网络.嵌入式系统嵌入式系统简单的在线和实时跟踪 (SORT)台湾本土 (TNCs) 的产品你只看一次 (YOLO)

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科学领域:

  • 农业技术 农业技术
  • 动物行为分析 动物行为分析
  • 计算机视觉在农业中的应用

背景情况:

  • 传统的群监测依赖于手动巡逻,这是劳动密集型的,增加了病原体风险.
  • 的分散和移动是群体健康和福利的关键指标.
  • 台湾本土 (TNCs) 在健康时表现出高体力活动,使运动分析至关重要.

研究的目的:

  • 开发一个自动预警系统,用于检测商业养场的异常分散和移动模式.
  • 为了降低劳动力成本,并最大限度地降低与手动农场监控相关的病原体引入的风险.
  • 为农民提供及时的警报,对影响群的潜在健康风险和环境危险进行警报.

主要方法:

  • 利用嵌入式系统在10周内捕获大约2万家跨国公司的空中视频录像.
  • 雇佣了一个You Only Look Once-version 7微小 (YOLOv7-tiny) 对象检测模型,用于准确识别.
  • 使用最近邻居指数 (NNI) 计算群落分散,使用简单的在线和实时跟踪 (SORT) 算法计算移动.
  • 通过自回归集成移动平均 (ARIMA) 和SARIMAX模型来确定异常检测的正常分散和运动范围.

主要成果:

  • YOLOv7微型模型在检测中实现了98.2%的精度.
  • 该SORT算法证明了95.3%的多重对象跟踪精度.
  • 在ARIMA和SARIMAX模型中,分散和移动的预测误差很低 (分别为3.71%和13.39%的MAPE).
  • 该系统成功地发现了与正常分散和运动模式的偏差.

结论:

  • 拟议的自动化系统有效地监测群的行为,比手工方法提供了显著的改进.
  • 这项技术通过减少人流和潜在的病原体引入来提高农场生物安全.
  • 该系统为早期发现大规模养殖中健康问题和环境问题提供了有价值的工具.