Xiuguo Zou, Qiaomu Ren, Hongyi Cao, Yan Qian, Shuaitang Zhang, Journal of Information Processing Systems Vol. 16, No. 2, pp. 435-446, Apr. 2020  

https://doi.org/10.3745/JIPS.02.0133
Keywords: High Dimensional Data, Machine Learning, Spectral Reflectance, Tea Diseases
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Abstract

With the ability to learn rules from training data, the machine learning model can classify unknown objects. At the same time, the dimension of hyperspectral data is usually large, which may cause an over-fitting problem. In this research, an identification methodology of tea diseases was proposed based on spectral reflectance and machine learning, including the feature selector based on the decision tree and the tea disease recognizer based on random forest. The proposed identification methodology was evaluated through experiments. The experimental results showed that the recall rate and the F1 score were significantly improved by the proposed methodology 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 learn from high dimensional data so as to achieve the non-destructive and efficient identification of different tea diseases. This research provides a new idea for the feature selection of high dimensional data and the nondestructive identification of crop diseases.

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