Abstract
This paper examines different methods of producing real-time Agtron index outputs for coffee bean baking. The goal is to provide an optimal roasting output based on the required profile, increasing baking accuracy over the commonly used time-temperature method. Although the Agtron baking degree is based on the caramel infrared index, it is also highly correlated with color and shape information. Experimentally, a baking color was sub-divided into ten categories (grades), images were taken with a common color camera, then a deep learning convolutional neural network performed analysis. Based on the LenNet architecture and parameters, this study develops a “convolution neural network for coffee bean baking identification” and develops a time-sequential binary classification model (TSBC) based on the time-decreasing characteristics of baking. The resultant system correctly determines the baking grades.
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Wu, QH., Shen, DF. (2019). Color Video and Convolutional Neural Networks Deep Learning Based Real-Time Agtron Baking Level Estimation Method. In: Chang, CY., Lin, CC., Lin, HH. (eds) New Trends in Computer Technologies and Applications. ICS 2018. Communications in Computer and Information Science, vol 1013. Springer, Singapore. https://doi.org/10.1007/978-981-13-9190-3_21
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DOI: https://doi.org/10.1007/978-981-13-9190-3_21
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