应用人工智能对烘焙过程中咖啡豆成熟度进行分类

Dede Herman Suryana, Wahyu Kusuma Raharja
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引用次数: 0

摘要

咖啡是一种全世界都很受欢迎的饮料。人们常常根据咖啡的香气和味道来判断咖啡的好坏。咖啡的品质受咖啡豆烘焙过程中各种参数的影响。烘焙是至关重要的一步,绿咖啡豆在高温下加热,经历水解、聚合和热解等化学反应。烘烤过程中的颜色变化是由美拉德和焦糖化反应产生的类黑素引起的,也影响了风味特征。因此,对咖啡豆的成熟度进行准确的分级是十分必要的。在超级计算机技术的发展中,特别是高速GPU微处理器和大容量内存的出现,使得人工智能算法得到了广泛的应用。智能机器的研究已经在进行,以创造类似人类智能的系统。它的一个应用是在烘焙过程中识别咖啡豆的成熟度。在本研究中,利用ROI (Region Of Interest)和RGB颜色特征对图像进行分割,识别每个咖啡豆图像的特征。此外,在分类阶段使用CNN(卷积神经网络),并将该模型实现到Android智能手机设备中,以检测被烘焙咖啡豆的类型。经过100次epoch的训练过程,该模型的训练损失为0.12,训练准确率为94.79%。该模型能够从测试数据中对图像进行分类,平均准确率为85.83%,损失值为0.35。
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Applying Artificial Intelligence to Classify the Maturity Level of Coffee Beans During Roasting
Coffee is a highly popular beverage worldwide. The quality of coffee is often judged based on its aroma and taste. Good coffee quality is influenced by various parameters during the coffee bean roasting process. Roasting is a crucial step where green coffee beans are heated at high temperatures, undergoing chemical reactions such as hydrolysis, polymerization, and pyrolysis. The color changes during the roasting process are caused by melanoidin, which results from Maillard and caramelization reactions, also impacting the flavor profile. Therefore, it is essential to accurately classify the level of coffee bean maturity. In the development of supercomputer technology, particularly with high-speed GPU microprocessors and large memory capacities, artificial intelligence algorithms have been widely implemented in various applications. Research on smart machines has been conducted to create systems resembling human intelligence. One of its applications is in recognizing the maturity level of coffee beans during roasting. In this study, image segmentation using ROI (Region Of Interest) and RGB color features are utilized to identify the characteristics of each coffee bean image. Additionally, CNN (Convolutional Neural Network) is employed for the classification stage, and this model is implemented into an Android smartphone device to detect the type of coffee bean being roasted. After the training process with 100 epochs, the model achieved a loss of 0.12 and a training accuracy of 94.79%. The model is capable of classifying images from the test data with an average accuracy of 85.83% and a loss value of 0.35.
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