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

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.

Vocalization of broilers can be used to identify their sex and genetic strain

In order to reach higher broiler performance, farmers target losses reduction. One way to make this possible is by rearing sexed broilers as male and female present diverse performance due to their physiological differences. Birds from different genetic strain also have a distinct performance at the same age. Considering that sexed flocks may present higher performance this study aimed to identify one-day-old chicks’ sex throughout their vocalization. This research also investigated the possibility of identifying the genetic strain by their vocalization attributes. A total of 120 chicks, half of them were from Cobb® genetic strain and the other half from Ross® genetic strain. From each group, a total of 30 were males and 30 females, which were previously separated by sex using their secondary physiological characteristics at the hatchery. Vocalizations audio recording was done inside a semi-anechoic chamber using a unidirectional microphone connected to an audio input of a digital recorder. Vocalizations were recorded for two minutes. Acoustic characteristics of the sounds were analyzed being calculated the fundamental frequency Pitch, the sound intensity, the first formant, and second formant. Results indicated that the vocalizations of both sexes could be identified by the second formant, and the genetic strain was detected by both the second formant and the Pitch.