Predicting carcass cut yields in cattle from digital images using artificial intelligence,Meat Science

Deep Learning (DL) has proven to be a successful tool for many image classification problems but has yet to be applied to carcass images. The aim of this study was to train DL models to predict carcass cut yields and compare predictions to more standard machine learning (ML) methods. Three approaches were undertaken to predict the grouped carcass cut yields of Grilling cuts and Roasting cuts from a large dataset of 54,598 and 69,246 animals respectively. The approaches taken were (1) animal phenotypic data used as features for a range of ML algorithms, (2) carcass images used to train Convolutional Neural Networks, and (3) carcass dimensions measured directly from the carcass images, combined with the associated phenotypic data and used as feature data for ML algorithms. Results showed that DL models can be trained to predict carcass cuts yields but an approach that uses carcass dimensions in ML algorithms performs slightly better in absolute terms.



深度学习 (DL) 已被证明是解决许多图像分类问题的成功工具,但尚未应用于尸体图像。本研究的目的是训练深度学习模型来预测屠体产量,并将预测结果与更标准的机器学习 (ML) 方法进行比较。采取了三种方法来预测分别来自 54,598 和 69,246 只动物的大型数据集的烤肉和烤肉的分组屠体产量。所采用的方法是 (1) 用作一系列 ML 算法特征的动物表型数据,(2) 用于训练卷积神经网络的尸体图像,以及 (3) 直接从尸体图像测量的尸体尺寸,并结合相关的表型数据并用作 ML 算法的特征数据。


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