The objective of this project was to develop a computer vision system (CVS) for objective measurement of pork loin under industry speed requirement. Color images of pork loin samples were acquired using a CVS. Subjective color and marbling scores were determined according to the National Pork Board standards by a trained evaluator. Instrument color measurement and crude fat percentage were used as control measurements. Image features (18 color features; 1 marbling feature; 88 texture features) were extracted from whole pork loin color images. Artificial intelligence prediction model (support vector machine) was established for pork color and marbling quality grades. The results showed that CVS with support vector machine modeling reached the highest prediction accuracy of 92.5% for measured pork color score and 75.0% for measured pork marbling score. This research shows that the proposed artificial intelligence prediction model with CVS can provide an effective tool for predicting color and marbling in the pork industry at online speeds.

该项目的目的是开发一种计算机视觉系统（CVS），用于在工业速度要求下客观测量猪里脊肉。猪腰肉样本的彩色图像使用CVS采集。主观的颜色和大理石花纹得分由训练有素的评估员根据国家猪肉局的标准确定。仪器颜色测量和粗脂肪百分比用作对照测量。从整个猪腰彩色图像中提取图像特征（18个颜色特征； 1个大理石花纹特征； 88个纹理特征）。建立了猪肉颜色和大理石花纹质量等级的人工智能预测模型（支持向量机）。结果表明，采用支持向量机建模的CVS对猪肉色度得分的最高预测准确度为92.5％，最高为75。测得的猪肉大理石花纹分数为0％。这项研究表明，提出的带有CVS的人工智能预测模型可以为在线速度下的猪肉行业中颜色和大理石花纹的预测提供有效的工具。