Identification of Anoectochilus Roxburghii Origins Based on Imbalanced Dataset

Anoectochilus roxburghii from different origins has different nutritional content and different price. Achieving origin identification is of great significance to the development and standardization of the Anoectochilus roxburghii industry However, it is influenced by complex factors, including the specific strain and growth environment. Achieving a high accuracy in origin identification presents significant challenges and may not always meet the stringent requirements. To account for the unique characteristics of Anoectochilus roxburghii dataset from different origins, such as limited sample size, imbalanced samples, and numerous sample interference factors, a method based on improved SMOTE and CatBoost is designed to address the need for precise origin identification. First, a Fourier transform near infrared spectrometer was used to collect sample information of Anoectochilus roxburghii from three different origins, and then the improved SMOTE algorithm was used to balance the dataset. Finally, CatBoost classifier was used to identify the different origins. Comparative experimental results show that the method proposed in this article has the highest identification accuracy, reaching more than 97%, which is 6.9% and 2.8% higher than using original data and the original SMOTE algorithm respectively. The model constructed can efficiently identify Anoectochilus roxburghii of different origins and can be served as a useful reference for quality supervision of Anoectochilus roxburghii.