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在多对母猪中进行实例细分和集体学习,用于在多对母猪中自动检测温度

Hongxiang Xue^1,2, Mingxia Shen^2,3, Yuwen Sun^1,2

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

Sensors (Basel, Switzerland)

|November 25, 2023

概括

本研究介绍了一种自动化方法,用于使用阴道细分和预测算法监测母猪核心体温. 这种方法提高了猪健康管理的准确性和效率.

科学领域:

动物科学动物科学
兽医医学兽医医学兽医医学
生物技术是生物技术.

背景情况:

核心体温对于母猪健康监测至关重要.
直肠温度计是常见的,但劳动密集型和不卫生.
目前的方法缺乏自动化和精度.

研究的目的:

开发一种用于猪温度监测的自动化系统.
与传统方法相比,提高准确性和效率.
为了实现猪场的自主温度跟踪.

主要方法:

使用了YOLOv5s和DeepLabv3+结合的细分网络来识别阴道区域.
集成的CBAM注意力和MobileNetv2用于准确和快速的细分.
开发了一个使用自适应性遗传算法-随机森林 (AGA-RF) 回归算法的温度预测模型.

主要成果:

实现了91.50%的阴道细分IOU.
预测直肠温度的平均平方误差 (MSE) 为0.114°C,平均绝对误差 (MAE) 为0.191°C,R平方 (R2) 为0.845.
证明了自主监控的高可靠性和实用性.

结论:

拟议的自动化系统为母猪温度监测提供了可靠和实用的解决方案.
这种方法解决了手动直肠温度计的局限性.
促进猪生产中的高效和自主健康管理.

关键词:

组合学习组合学习实例细分实例细分实例细分播种播种播种播种播种播种播种温度监测温度监测温度监测

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