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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제16권 제2호
- 발행연도
- 2020.1
- 수록면
- 435 - 446 (12page)
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초록· 키워드
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With the ability to learn rules from training data, the machine learning model can classify unknown objects. Atthe same time, the dimension of hyperspectral data is usually large, which may cause an overfitting problem. In this research, an identification methodology of tea diseases was proposed based on spectral reflectance andmachine learning, including the feature selector based on the decision tree and the tea disease recognizer basedon random forest. The proposed identification methodology was evaluated through experiments. Theexperimental results showed that the recall rate and the F1 score were significantly improved by the proposedmethodology in the identification accuracy of tea disease, with average values of 15%, 7%, and 11%, respectively. Therefore, the proposed identification methodology could make relatively better feature selection and learnfrom high dimensional data so as to achieve the nondestructive and efficient identification of different teadiseases. This research provides a new idea for the feature selection of high dimensional data and the nondestructiveidentification of crop diseases.
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참고문헌 (34)
참고문헌 신청
X. Wan / 2015 / Research advance on tea biochemistry / Journal of Tea Science 35(1):1~10
Z. Luo / 2016 / Advanced in research and application of sex pheromone of tea(Camellia sinensis)pest / Journal of Tea Science 36(3):229~236
P. Peng / 2010 / Review and prospect on forecasting of tea pests and diseases / Southwest China Journal of Agricultural Sciences 23(5):1742~1745
S. Cheng / 2014 / Fast detection methods for crop disease infection period using spectral and imaging technology
Y. Hong / 2006 / Progress in real-time quantitative PCR technique / International Journal of Epidemiology and Infectious Disease 33(3):161~166
Z. Luo / 2016 / Advanced in research and application of sex pheromone of tea(Camellia sinensis)pest / Journal of Tea Science 36(3):229~236
P. Peng / 2010 / Review and prospect on forecasting of tea pests and diseases / Southwest China Journal of Agricultural Sciences 23(5):1742~1745
S. Cheng / 2014 / Fast detection methods for crop disease infection period using spectral and imaging technology
Y. Hong / 2006 / Progress in real-time quantitative PCR technique / International Journal of Epidemiology and Infectious Disease 33(3):161~166
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