A fruits recognition system based on a modern deep learning technique; Journal of Physics: Conference Series; Vol. 1327 : Innovations in Non-Destructive Testing (SibTest 2019)
| Parent link: | Journal of Physics: Conference Series Vol. 1327 : Innovations in Non-Destructive Testing (SibTest 2019).— 2019.— [012050, 5 р.] |
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| المؤلف الرئيسي: | |
| مؤلف مشترك: | |
| مؤلفون آخرون: | |
| الملخص: | Title screen The popular technology used in this innovative era is Computer vision for fruit recognition. Compared to other machine learning (ML) algorithms, deep neural networks (DNN) provide promising results to identify fruits in images. Currently, to identify fruits, different DNN-based classification algorithms are used. However, the issue in recognizing fruits has yet to be addressed due to similarities in size, shape and other features. This paper briefly discusses the use of deep learning (DL) for recognizing fruits and its other applications. The paper will also provide a concise explanation of convolution neural networks (CNNs) and the EfficientNet architecture to recognize fruit using the Fruit 360 dataset. The results show that the proposed model is 95% more accurate. |
| اللغة: | الإنجليزية |
| منشور في: |
2019
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://doi.org/10.1088/1742-6596/1327/1/012050 http://earchive.tpu.ru/handle/11683/57042 |
| التنسيق: | الكتروني فصل الكتاب |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=661313 |