Recognition of Livestock Disease Using Adaptive Neuro-Fuzzy Inference System

Ricky Mohanty, Subhendu Kumar Pani and Ahmad Taher Azar
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Ricky Mohanty: Orissa Engineering College, India
Subhendu Kumar Pani: Krupajal Computer Academy, Bhubaneswar, India
Ahmad Taher Azar: College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia & Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

International Journal of Sociotechnology and Knowledge Development (IJSKD), 2021, vol. 13, issue 4, 101-118

Abstract: 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.

Date: 2021
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