Bamboo variety recognition method based on artificial intelligence deep learning
A deep learning and artificial intelligence technology, applied in character and pattern recognition, instruments, biological neural network models, etc., to avoid sampling errors and improve accuracy
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[0093]Take the complete bamboo ring at the internode part of the high breast of the bamboo, use the method provided by the embodiment of the present invention to train the SENet model after polishing and scanning, and then use the trained SENet model to process the bamboo pictures to be classified. The model training takes 24 hours, and the recognition time for each picture is about 1 second. The results show that the method provided by the embodiment of the present invention can accurately identify the bamboo species for the bamboo pictures to be classified. The probability distribution of bamboo species obtained by using the SENeT model is as follows:
[0094] Diaoluo Ni bamboo: Diaoluo Ni bamboo 99.96%, Ci bamboo 0.02%, Pao bamboo 0.01%, Datou Dian bamboo 0.01%, Moso bamboo 0.01%, Ma bamboo 0.0%, Thai bamboo 0.0%, Apas bamboo 0.0%, sand Luodan bamboo 0.0%, pear bamboo 0.0%;
[0095] Datoudian bamboo: Datoudian bamboo 100.0%, Saluo single bamboo 0.0%, Moso bamboo 0.0%, Apas...
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