Vocalization data mining for estimating swine stress conditions

Abstract

This study aimed to identify differences in swine vocalization pattern according to animal gender and different stress conditions. A total of 150 barrow males and 150 females (Dalland ® genetic strain), aged 100 days, were used in the experiment. Pigs were exposed to different stressful situations: thirst (no access to water), hunger (no access to food), and thermal stress (THI exceeding 74). For the control treatment, animals were kept under a comfort situation (animals with full access to food and water, with environmental THI lower than 70). Acoustic signals were recorded every 30 minutes, totaling six samples for each stress situation. Afterwards, the audios were analyzed by Praat ® 5.1.19 software, generating a sound spectrum. For determination of stress conditions, data were processed by WEKA ® 3.5 software, using the decision tree algorithm C4.5, known as J48 in the software environment, considering cross-validation with samples of 10% (10-fold cross-validation). According to the Decision Tree, the acoustic most important attribute for the classification of stress conditions was sound Intensity (root node). It was not possible to identify, using the tested attributes, the animal gender by vocal register. A decision tree was generated for recognition of situations of swine hunger, thirst, and heat stress from records of sound intensity, Pitch frequency, and Formant 1.

Data mining as a hatchery process evaluation tool

“…On the other hand, data mining (DM) explains the acquisition of knowledge in terms of logical rules, such as the construction of classification trees. DM has been successfully applied in animal science to animal environment (Vale et al, 2010), laying breeders' production (Ferreira et al, 2013), breeding certification (Vieira et al, 2015), animal welfare (Moi et al, 2014), and egg quality prediction (Orhan et al, 2016).…”

The Use of Vocalization Signals to Estimate the Level of Pain in Piglets

“…Techniques to determine pain and welfare in farmed animals might be well understood to avoid misinterpretation and get more accurate results to recognize the real status of the animal (Fitzpatrick et al, 2006). Machine-learning techniques including data mining improved the discovery of knowledge in the livestock production (Nääs et al, 2008;Moi et al, 2014;Pandorfi et al, 2011). Amongst the tasks of data mining, the classification allows the generation, starting from a set of examples (training), of a classifier able to classify a new sample in its class.…”

Effect of Artificial Piglet Suckling Sounds on Behavior and Performance of Piglets and Adrenal Responses of Sows

“…Previous studies have demonstrated a relationship between stress and suckling sounds [ 2 , 8 , 9 ]. Major stressor would be negatively affecting sow performances if suckling sounds are played all day round [ 8 ].…”

“…Previous studies have demonstrated a relationship between stress and suckling sounds [ 2 , 8 , 9 ]. Major stressor would be negatively affecting sow performances if suckling sounds are played all day round [ 8 ]. The higher level of 90 decibels of suckling sounds would lead a deviant of sow feeding patterns and other maternal behaviors [ 2 ] which are negatively impact on the condition of sows after lactating period e.g., an increased weaning to first estrous interval (WFEI) [ 9 ].…”

“…The higher level of 90 decibels of suckling sounds would lead a deviant of sow feeding patterns and other maternal behaviors [ 2 ] which are negatively impact on the condition of sows after lactating period e.g., an increased weaning to first estrous interval (WFEI) [ 9 ]. All of which can increase glucocorticoid production from the adrenal glands [ 2 , 8 , 9 ]. Chronic glucocorticoid exposure can then lead to problems, including abnormal behavior, suppressed immune function, poor population performance, and disruption of reproductive hormone production [ 9 – 11 ].…”