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早期检测鸟类疾病基于温度和人工智能
Mohammad Sadeghi1, Ahmad Banakar1, Saeid Minaei1
1Biosystems Engineering Department, Tarbiat Modares University, Tehran 14117-13116, Iran.
Animals : an open access journal from MDPI
|July 29, 2023
概括
这项研究引入了一种使用热成像和机器学习的非侵入性方法,用于检测禽类疾病,如牛卡尔病 (ND) 和禽流感 (AI) 在 brojlers. 该系统实现了高精度,使得早期干预和改善家禽养殖.
科学领域:
- 精准畜牧养殖 精准畜牧养殖 精准畜牧养殖
- 鸟类健康监测 鸟类健康监测
- 机器学习在农业中的应用
背景情况:
- 非侵入性监测对于动物福利和禽畜养殖中早期疾病检测至关重要.
- 动物活动和生理数据,如热信号,可以表明健康状况.
- 及时识别像纽卡斯尔病 (ND) 和禽流感 (AI) 这样的疾病对于群体管理至关重要.
研究的目的:
- 开发和评估一种使用热成像和机器学习识别鸟类疾病的非侵入性方法.
- 要区分健康的肉和感染ND或AI的肉.
- 建立针对禽病爆发的早期预警系统.
主要方法:
- 使用热成像来捕获感染ND,AI或健康对照的 brojler chickens 的数据.
- 使用MATLAB处理热图像,在消除噪音和去除背景后提取23个统计特征.
- 开发和比较支持向量机 (SVM) 和人工神经网络 (ANN) 分类器,结合Dempster-Shafer数据融合证据理论.
主要成果:
- 与ANN相比,SVM分类器在鸟类疾病分类方面表现优越.
- 基于SVM的框架实现了对禽流感 (AI) 的97.2%准确率和对纽卡斯尔病 (ND) 的100%准确率.
- 确切的疾病分类是在感染后24小时内实现的.
结论:
- 拟议的热图像处理和机器学习框架为早期识别鸟类疾病提供了一种创新和准确的方法.
- 这种非侵入性方法支持及时干预,有助于改善动物健康和精确畜牧业.
- 高准确率表明了这项技术在家禽实时疾病监测方面的潜力.
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