Abstract
Herbal plant image identification application is expected to be able to help users without specialized knowledge about botany and plant systematics to figure out the useful information, thus it has become an interdisciplinary focus in both botanical taxonomy and computer vision. A computer vision aided herbal plan identification system has been developed to meet the demand of recognizing and identifying herbal plants rapidly. In this paper, the first herbal plant image dataset collected from 2 sources: take by mobile phone in natural scenes and craw from the Internet, which contains approximately 5,000 images of 4 herbal plant species in Vietnam. A pre-trained deep learning model called ResNet50 is used to extract features from the images. Since it was assumed that data was not sufficient before the learning process, the extracted features are separated into five same part. Then an incremental learning process took place using extracted features as input data. The accuracy results increase from 72,3% to 91,2% and also demonstrate that the system is likely to be improved and make practical sense when more data is added including future types.
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Hung, P.D., Su, N.T. (2020). Incremental Learning for Classifying Vietnamese Herbal Plant. In: Dang, T.K., Küng, J., Takizawa, M., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2020. Communications in Computer and Information Science, vol 1306. Springer, Singapore. https://doi.org/10.1007/978-981-33-4370-2_31
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DOI: https://doi.org/10.1007/978-981-33-4370-2_31
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