Using Deep Learning for Morphological Classification in Pigs with a
  Focus on Sanitary Monitoring
Eduardo Bedin Junior Silva Souza Gabriel Toshio Hirokawa Higa Alexandre Pereira Charles Kiefer Newton Loebens Hemerson Pistori
The aim of this paper is to evaluate the use of D-CNN (Deep Convolutional
Neural Networks) algorithms to classify pig body conditions in normal or not
normal conditions, with a focus on characteristics that are observed in
sanitary monitoring, and were used six different algorithms to do this task.
The study focused on five pig characteristics, being these caudophagy, ear
hematoma, scratches on the body, redness, and natural stains (brown or black).
The results of the study showed that D-CNN was effective in classifying
deviations in pig body morphologies related to skin characteristics. The
evaluation was conducted by analyzing the performance metrics Precision,
Recall, and F-score, as well as the statistical analyses ANOVA and the
Scott-Knott test. The contribution of this article is characterized by the
proposal of using D-CNN networks for morphological classification in pigs, with
a focus on characteristics identified in sanitary monitoring. Among the best
results, the average Precision metric of 80.6\% to classify caudophagy was
achieved for the InceptionResNetV2 network, indicating the potential use of
this technology for the proposed task. Additionally, a new image database was
created, containing various pig's distinct body characteristics, which can
serve as data for future research.