Efficiency of distinct data mining algorithms for classifying stress level in piglets from their vocalization

Engenharia Agrícola

Alexandra F. Da S. Cordeiro²  Irenilza De A. Nääs¹  Stanley R. De M. Oliveira¹  Fabio Violaro¹  Andréia C. M. De Almeida¹ 

[1] ,Faculdade de Engenharia Agrícola

关键词: vocal expression;    well-being;    level of pain;    pig;    expressão vocal;    bem-estar;    nível de dor;    suínos;   

DOI  :  10.1590/S0100-69162012000200001

来源: SciELO

【 摘 要 】

Among the challenges of pig farming in today's competitive market, there is factor of the product traceability that ensures, among many points, animal welfare. Vocalization is a valuable tool to identify situations of stress in pigs, and it can be used in welfare records for traceability. The objective of this work was to identify stress in piglets using vocalization, calling this stress on three levels: no stress, moderate stress, and acute stress. An experiment was conducted on a commercial farm in the municipality of Holambra, São Paulo State , where vocalizations of twenty piglets were recorded during the castration procedure, and separated into two groups: without anesthesia and local anesthesia with lidocaine base. For the recording of acoustic signals, a unidirectional microphone was connected to a digital recorder, in which signals were digitized at a frequency of 44,100 Hz. For evaluation of sound signals, Praat® software was used, and different data mining algorithms were applied using Weka® software. The selection of attributes improved model accuracy, and the best attribute selection was used by applying Wrapper method, while the best classification algorithms were the k-NN and Naive Bayes. According to the results, it was possible to classify the level of stress in pigs through their vocalization.

【 授权许可】

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