Discrimination of tea plant variety using in-situ multispectral imaging system and multi-feature analysis

• Published in: Computers and electronics in agriculture Vol. 202; p. 107360
• Main Authors: Cao, Qiong, Yang, Guijun, Wang, Fan, Chen, Longyue, Xu, Bo, Zhao, Chunjiang, Duan, Dandan, Jiang, Ping, Xu, Ze, Yang, Haibin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2022
• Subjects: Camellia sinensis L; Discrimination; Multi-feature analysis; Multispectral imaging; Tea plant variety; Multispectral imaging; Discrimination; Tea plant variety; Camellia sinensis L; Multi-feature analysis
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2022.107360

•Multispectral data are effective in discriminating tea plant varieties.•Texture information are essential elements in discriminating tea plant varieties.•Data fusion of multispectral image can improve tea plant discrimination rate. Discrimination of tea plant (Camellia sinensis L.) varieties is of significant value for the efficient management of cultivation and resources optimization of tea industry. Phenotypic and spectral characteristics of tea plants are important indicators for determining the quality of tea cultivars to a certain extent. However, few studies have used spectral image information to identify tea plant varieties. The aim of this research was the discrimination of 16 types of high-yield tea plant varieties using a multispectral camera. This methodology involved image registration, calibration, segmentation, information extraction, and data fusion. The hue (H), saturation (S), and value (V), texture information, and several spectral vegetation indices were acquired from the multispectral image of the tea plant canopy. The successive projection algorithm (SPA) was used to analyze the original parameters. Three classification methods were applied to tea plant variety discrimination: Bayes discriminant analysis (BDA), support vector machine (SVM), and extreme learning machine (ELM). The results indicated that SPA based on fusing data combined with the SVM classification model, achieved a feasible method to identify tea plant varieties. Additionally, the method achieved accuracy in the training, test, and validation sets, reaching 97.00%, 90.52%, and 88.67%, respectively. This study proposed a new perspective on multispectral image information as an identifier of tea plant varieties. The explored model will be helpful for the development of portable instruments for commercial applications in variety identification and phenotype recognition of tea plants.