Abstract
The appearance quality of apple is one of the important indicators for consumers to purchase. At present, the classification process of apple is still completed artificially, which not only wastes human resources, but also easily causes subjective misclassification. This paper proposes a convolutional neural network model to classify defective and defect-free apples. Apple images are collected by the smartphone camera, each type of apple has 312 images. The number of apple images is expanded through data enhancement technology, and randomly divided into training set, validation set, and test set according to the ratio of 6:2:2. The final classification accuracy is 99.2%.
Supported by Key R&D projects of Shandong Province under grant 2019GNC106093, Shandong Agricultural machinery equipment R&D innovation plan project under grant 2018YF011, Key R&D projects of Shandong Province under grant 2019JZZY021005, Shandong Provincial Key R&D project 2017GGX10116.
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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Xu, Z., Shen, T., Bi, S., Zhao, Q. (2021). Apple Defect Detection Method Based on Convolutional Neural Network. In: Fu, W., Xu, Y., Wang, SH., Zhang, Y. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-030-82562-1_37
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DOI: https://doi.org/10.1007/978-3-030-82562-1_37
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