Accurate, non-destructive assessment of egg quality is critical for ensuring food safety, maintaining product standards, and operational efficiency in commercial poultry production. This paper introduces ELMF4EggQ, an ensemble learning framework that employs multimodal feature fusion to classify egg grade and freshness using only external attributes - image, shape, and weight. A novel, publicly available dataset of 186 brown-shelled eggs was constructed, with egg grade and freshness levels determined through laboratory-based expert assessments involving internal quality measurements, such as yolk index and Haugh unit. To the best of our knowledge, this is the first study to apply machine learning methods for internal egg quality assessment using only external, non-invasive features, and the first to release a corresponding labeled dataset. The proposed framework integrates deep features extracted from external egg images with structural characteristics such as egg shape and weight, enabling a comprehensive representation of each egg. Image feature extraction is performed using top-performing pre-trained CNN models (ResNet152, DenseNet169, and ResNet152V2), followed by PCA-based dimensionality reduction, SMOTE augmentation, and classification using multiple machine learning algorithms. An ensemble voting mechanism combines predictions from the best-performing classifiers to enhance overall accuracy. Experimental results demonstrate that the multimodal approach significantly outperforms image-only and tabular (shape and weight) only baselines, with the multimodal ensemble approach achieving 86.57% accuracy in grade classification and 70.83% in freshness prediction. All code and data are publicly available at https://github.com/Kenshin-Keeps/Egg_Quality_Prediction_ELMF4EggQ, promoting transparency, reproducibility, and further research in this domain.

翻译：鸡蛋品质的准确、无损评估对于保障食品安全、维持产品标准以及提高商业家禽生产运营效率至关重要。本文提出ELMF4EggQ，一种采用多模态特征融合的集成学习框架，仅利用外部属性——图像、形状和重量——对鸡蛋等级和新鲜度进行分类。本研究构建了一个新颖的、公开可用的包含186枚褐壳鸡蛋的数据集，其鸡蛋等级和新鲜度水平通过基于实验室的专家评估确定，评估涉及蛋黄指数和哈夫单位等内部品质测量。据我们所知，这是首个仅利用外部、非侵入性特征应用机器学习方法评估鸡蛋内部品质的研究，也是首个发布相应标注数据集的研究。所提出的框架将从外部鸡蛋图像提取的深度特征与鸡蛋形状、重量等结构特征相融合，从而实现对每个鸡蛋的全面表征。图像特征提取使用性能优异的预训练CNN模型（ResNet152、DenseNet169和ResNet152V2）完成，随后进行基于PCA的降维、SMOTE数据增强以及使用多种机器学习算法的分类。一个集成投票机制结合了性能最佳分类器的预测，以提高整体准确率。实验结果表明，多模态方法显著优于仅使用图像或仅使用表格（形状和重量）数据的基线方法，其中多模态集成方法在等级分类中达到86.57%的准确率，在新鲜度预测中达到70.83%的准确率。所有代码和数据均在https://github.com/Kenshin-Keeps/Egg_Quality_Prediction_ELMF4EggQ 公开提供，以促进该领域的透明度、可重复性及进一步研究。