Using Deep Learning for Morphological Classification in Pigs with a Focus on Sanitary Monitoring
This work addresses sanitary monitoring in pig farming, but it is incremental as it applies existing D-CNN methods to a new dataset.
This paper tackled the problem of classifying pig body conditions for sanitary monitoring using deep convolutional neural networks (D-CNNs), achieving an average precision of 80.6% for detecting caudophagy with the InceptionResNetV2 network.
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.