基于自适应神经模糊推理系统的家畜疾病识别

Ricky Mohanty, S. Pani, A. Azar
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引用次数: 5

摘要

畜禽健康管理系统的基本理念是通过采集鸟类发声等生物学特征来调查其健康状况。这个主题是在四种不同的牲畜身上实施的,以治疗它们的支气管炎疾病。本文包括健康和不健康牲畜的音频特征。特别是,安全的音频健康功能被纳入平台,以使用牲畜语音信息自发地检查和总结,以识别病禽。进行了为期一个月的长期识别实验研究,采用自适应神经模糊推理系统(ANFIS)对一组病禽的识别准确率达到99%左右。在这方面,ANFIS的识别精度优于人工神经网络的性能。这是研究人员调查和构成疾病可治愈性或根除不可治愈疾病的证据的可靠方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recognition of Livestock Disease Using Adaptive Neuro-Fuzzy Inference System
The livestock health management system is based on the principal concept to investigate bird health status by collecting biological traits like their sound utterance. This theme is implemented on four different species of livestock to cure them of bronchitis disease. This paper includes the audio features of both healthy and unhealthy livestock. Particularly, the secure audio-wellbeing features are incorporated into the platform to spontaneously examine and conclude using livestock voice information to recognize diseased birds. One month of long-term recognition experimental studies has been conducted where the recognition accuracy of the set of diseased birds was about 99% using adaptive neuro-fuzzy inference system (ANFIS). This recognition accuracy of ANFIS in this regard is better than the performance of an artificial neural network. This is a reliable way for researchers to investigate and constitute evidence of disease curability or eradication of incurable ones.
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