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Pork consumption grows about 5% per year, especially in developing countries. Ensuring food safety within ethical standards of meat production is a growing consumer demand. The present study aimed to develop a model to predict stress in piglets based on the infrared skin temperature (IST. A total of 40 piglets (20 males and 20 females) from 1 to 22 weeks under different stress conditions had the skin temperature recorded during the farrowing and nursery phases. The assessment of the thermal images was done using an infrared thermography camera. Thermograms were taken at ambient air temperatures ranging from 24 to 30 oC. The studied stresses were hunger, pain, thirst, heat/cold, and the normal (baseline). The attributes considered in the analysis were classified using data mining having the stress condition as the target. Since the surface temperature required imaging technique and it is subject to certain uncertainties, the paraconsistent logic was applied. After obtaining the results, we applied fuzzy logic to verify if the output of paraconsistent logic was like that of using the fuzzy inference approach. Applying data mining the decision tree that used minimum temperature had better accuracy for cold, hunger and normal attributes. The pain and thirst attributes had better precision in the model with the maximum surface temperature and the sex of the pig. In the fuzzy approach, the best degree of pertinence was shown related to thirst. A software was developed, and results indicate a promising assessment of stress condition in piglets using infrared skin temperature.

Pork consumption grows about 5% per year in developing countries. Ensuring food safety within ethical standards of meat production is a growing consumer’ demand. The present study aimed to develop a model to predict stress in piglets based on the infrared skin temperature (IST) using machine learning and the paraconsistent logic. A total of 72 piglets (32 males and 40 females) from 1 to 52 days old had the infrared skin temperature recorded during the farrowing and nursery phases under different stress conditions (pain, cold/heat, hunger, and thirst). The assessment of the thermal images was done using an infrared thermography camera. Thermograms were taken at ambient air temperatures ranging from 24 to 30 °C. The minimum infrared skin temperature (IST min) and the maximum infrared skin temperature (ISTmax) and the piglet sex were used as attributes to find the stress conditions (target). The attributes considered in the analysis were classified using the data mining method. The imaging technique is subject to certain contradictions and uncertainties that require mathematical modeling. The paraconsistent logic was applied to extract the contradiction from the data. The stress condition that had higher accuracy in the detection was that predicted by the cold (100%) using the ISTmin, and ISTmin plus the piglet sex, and thirst (91%) using ISTmax and ISTmax plus the piglet sex. The highest prediction of hunger was found using ISTmin (86%). Although the model was precise in detecting those stresses, the other stressful conditions in piglets such as pain that had an accuracy equal or less than 50%. Results indicate a promising assessment of stress condition in piglets using infrared skin temperature. We suggest the inclusion of other attributes in the machine learning process to amplify the use of the model.