Summary of Using Deep Learning For Morphological Classification in Pigs with a Focus on Sanitary Monitoring, by Eduardo Bedin et al.
Using Deep Learning for Morphological Classification in Pigs with a Focus on Sanitary Monitoring
by Eduardo Bedin, Junior Silva Souza, Gabriel Toshio Hirokawa Higa, Alexandre Pereira, Charles Kiefer, Newton Loebens, Hemerson Pistori
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the application of Deep Convolutional Neural Networks (D-CNN) in classifying pig body conditions, focusing on characteristics observed in sanitary monitoring. Six algorithms are employed to classify five pig characteristics: caudophagy, ear hematoma, scratches, redness, and natural stains. The study evaluates the performance of D-CNN using metrics such as Precision, Recall, and F-score, as well as statistical analyses like ANOVA and the Scott-Knott test. The results demonstrate the effectiveness of D-CNN in classifying deviations in pig body morphologies related to skin characteristics. Notably, the InceptionResNetV2 network achieves an average Precision metric of 80.6% for caudophagy classification, highlighting the potential use of this technology for the proposed task. Additionally, a new image database is created, containing various pig body characteristics, which can serve as data for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computer programs called Deep Convolutional Neural Networks (D-CNN) to help classify pigs based on their physical condition. The researchers looked at six different ways of using these algorithms to identify five different things about pigs: whether they eat their own tails, have ear problems, have scratches or redness on their skin, and have natural stains like brown or black spots. The study showed that D-CNN can be very good at identifying when a pig’s physical condition is not normal. One of the algorithms used did especially well in identifying when pigs are eating their own tails, which could be useful for farmers who want to keep an eye on their animals. |
Keywords
» Artificial intelligence » Classification » Cnn » Precision » Recall
