Cheese production, a globally cherished culinary tradition, faces challenges in ensuring consistent product quality and production efficiency. The critical phase of determining cutting time during curd formation significantly influences cheese quality and yield. Traditional methods often struggle to address variability in coagulation conditions, particularly in small-scale factories. In this paper, we present several key practical contributions to the field, including the introduction of CM-IDB, the first publicly available image dataset related to the cheese-making process. Also, we propose an innovative artificial intelligence-based approach to automate the detection of curd-firming time during cheese production using a combination of computer vision and machine learning techniques. The proposed method offers real-time insights into curd firmness, aiding in predicting optimal cutting times. Experimental results show the effectiveness of integrating sequence information with single image features, leading to improved classification performance. In particular, deep learning-based features demonstrate excellent classification capability when integrated with sequence information. The study suggests the suitability of the proposed approach for integration into real-time systems, especially within dairy production, to enhance product quality and production efficiency.

中文翻译：

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通过计算机视觉和机器学习技术检测奶酪制作中的凝固时间


奶酪生产是全球珍视的烹饪传统，在确保一致的产品质量和生产效率方面面临着挑战。在凝乳形成过程中确定切割时间的关键阶段会显著影响奶酪的质量和产量。传统方法通常难以解决凝结条件的可变性，尤其是在小型工厂中。在本文中，我们介绍了对该领域的几个关键实际贡献，包括引入 CM-IDB，这是第一个与奶酪制作过程相关的公开可用的图像数据集。此外，我们还提出了一种基于人工智能的创新方法，结合使用计算机视觉和机器学习技术，在奶酪生产过程中自动检测凝乳凝固时间。所提出的方法提供了对凝乳硬度的实时洞察，有助于预测最佳切割时间。实验结果表明，将序列信息与单个图像特征整合是有效的，从而提高了分类性能。特别是，基于深度学习的特征在与序列信息集成时表现出出色的分类能力。该研究表明，所提出的方法适合集成到实时系统中，尤其是在乳制品生产中，以提高产品质量和生产效率。