Abstract
This paper describes a system to infer the brand of apple considering physical features of its flesh. The system comprises a hardware to examine the apple’s physical features and a software with convolutional neural network (CNN) to classify an apple into any brand. When a sharp metal blade cuts the piece of the apple flesh, the load and the sound are measured. From these data, the computer generates an image consisting of the sound spectrogram and the color bar expressing the load change. The sound spectrogram has rich features of the apple flesh. The image is inputted to CNN to infer the brand of apple. In the experiment part, the authors validated the proposed system. The goal of our study is to construct a system to estimate the texture such as crunchiness or crispness. The system is applicable to the quality management of the brand of apples. For example, one apple randomly chosen from many apples could be examined by the present system in order to check the texture quality of the flesh.
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The authors would like to thank reviewers for giving us fruitful comments, and Maeda in MathWorks for giving us technical advice.
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Kato, S. et al. (2020). Apple Brand Classification Using CNN Aiming at Automatic Apple Texture Estimation. In: Barolli, L., Hellinckx, P., Natwichai, J. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2019. Lecture Notes in Networks and Systems, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-030-33509-0_76
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DOI: https://doi.org/10.1007/978-3-030-33509-0_76
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