利用深度学习进行猪的形态学分类，重点关注卫生监测
Using Deep Learning for Morphological Classification in Pigs with a Focus on Sanitary Monitoring
AI总结：
本文评估六种深度卷积神经网络在猪形态分类中的应用，重点关注卫生监测特征，最佳模型InceptionResNetV2对尾部噬咬分类精确率达80.6%，并创建了新图像数据库。
摘要：
本文旨在评估使用D-CNN（深度卷积神经网络）算法对猪身体状况进行分类，分为正常或非正常状态，重点关注卫生监测中观察到的特征，并使用了六种不同的算法来完成此任务。研究聚焦于猪的五个特征，即尾部噬咬、耳部血肿、身体抓痕、发红以及自然污渍（棕色或黑色）。研究结果表明，D-CNN在分类与皮肤特征相关的猪体形态偏差方面是有效的。评估通过分析性能指标精确率、召回率和F值，以及统计分析ANOVA和Scott-Knott检验进行。本文的贡献在于提出了使用D-CNN网络进行猪形态学分类，重点关注卫生监测中识别的特征。在最佳结果中，InceptionResNetV2网络在分类尾部噬咬方面达到了平均精确率80.6%，表明该技术在此任务中的潜在应用价值。此外，还创建了一个新的图像数据库，包含猪的各种不同身体特征，可为未来研究提供数据。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
