The identification of different meat cuts for labeling and quality control on production lines is still largely a manual process. As a result, it is a labor-intensive exercise with the potential for not only error but also bacterial cross-contamination. Artificial intelligence is used in many disciplines to identify objects within images, but these approaches usually require a considerable volume of images for training and validation. The objective of this study was to identify five different meat cuts from images and weights collected by a trained operator within the working environment of a commercial Irish beef plant. Individual cut images and weights from 7,987 meats cuts extracted from semimembranosus muscles (i.e., Topside muscle), post editing, were available. A variety of classical neural networks and a novel Ensemble machine learning approaches were then tasked with identifying each individual meat cut; performance of the approaches was dictated by accuracy (the percentage of correct predictions), precision (the ratio of correctly predicted objects relative to the number of objects identified as positive), and recall (also known as true positive rate or sensitivity). A novel Ensemble approach outperformed a selection of the classical neural networks including convolutional neural network and residual network. The accuracy, precision, and recall for the novel Ensemble method were 99.13%, 99.00%, and 98.00%, respectively, while that of the next best method were 98.00%, 98.00%, and 95.00%, respectively. The Ensemble approach, which requires relatively few gold-standard measures, can readily be deployed under normal abattoir conditions; the strategy could also be evaluated in the cuts from other primals or indeed other species.

中文翻译：

---

使用人工智能自动识别牛肉去骨线上半膜肌的肉切块


在生产线上识别不同肉类部位以进行标签和质量控制仍然主要是手动过程。因此，这是一项劳动密集型工作，不仅可能出现错误，还可能出现细菌交叉污染。人工智能在许多学科中被用来识别图像中的对象，但这些方法通常需要大量图像进行训练和验证。本研究的目的是根据训练有素的操作员在商业爱尔兰牛肉厂的工作环境中收集的图像和重量来识别五种不同的肉切块。从半膜肌（即上侧肌肉）中提取的 7,987 块肉块中提取的单独切割图像和重量，经过后期编辑后均可用。然后，各种经典神经网络和新颖的集成机器学习方法的任务是识别每个单独的切肉；这些方法的性能由准确度（正确预测的百分比）、精确度（正确预测的对象相对于识别为阳性的对象数量的比率）和召回率（也称为真阳性率或灵敏度）决定。一种新颖的集成方法优于包括卷积神经网络和残差网络在内的经典神经网络。新的 Ensemble 方法的准确度、精确度和召回率分别为 99.13%、99.00% 和 98.00%，而次优方法的准确度、精确度和召回率分别为 98.00%、98.00% 和 95.00%。集成方法需要相对较少的黄金标准措施，可以在正常屠宰场条件下轻松部署；该策略还可以通过其他原始动物或其他物种的砍伐来评估。