Abstract
This article discusses the automation of the cooking process of a deep-fried food item called Unniyappam. It involves the design of a machine capable of mass production along with the provision for monitoring the quality of the cooked product. The working of the machine involves pouring a fixed volume of the prepared batter into a mold immersed in boiling oil. The mold remains immersed in the boiling oil for a preset time, and the fried food products are automatically removed from the mold. The quality monitoring discussed here refers to a deep neural-network-based computer vision system to check the fried products for partially, optimally, or over fried conditions. The computer vision system uses a GoogLeNet, with its last fully connected layer and subsequent soft-max layer and classification layer modified to classify input images into three classes. The preset time of frying is changed to optimal value the output of the computer vision system.
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Antony, A., Antony, J., Martin, E., Benny, T., Vimal Kumar, V., Priya, S. (2021). Design and Development of an Automated Snack Maker with CNN-Based Quality Monitoring. In: Suma, V., Chen, J.IZ., Baig, Z., Wang, H. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-16-1395-1_15
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DOI: https://doi.org/10.1007/978-981-16-1395-1_15
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