Leaf phenotypic difference analysis and variety recognition of tea cultivars based on multispectral imaging technology

Abstract

Recognition of tea plant variety and grade is essential for tea germplasm resources protection. The rapid and accurate acquisition of phenotype of tea leaves is a crucial step in exploring the variety type, nutrition status, and yield prediction. Monitoring the phenotypic characteristics of tea leaves is necessary for intelligent tea germplasm management. This study analyzed phenotypic features of tea leaves based on multispectral imaging technology. Tea leaf images of 12242 sets from 25 different types, along with 61 groups of chemical characteristics of fresh tea leaves were obtained. A total of 92 indicators were extracted, and 38 indicators were screened using the successive projection algorithm and the shuffled frog leaping algorithm, which showed significant differences among different tea varieties. The phenotypic indexes of different tea varieties were analyzed, and a tea variety recognition model was established based on these indexes combined with gray wolf optimization-support vector machine algorithm. The average accuracy of the training, test, and validation sets were 99.74 %, 92.17 %, and 91.56 %, respectively. Additionally, quantitative evaluation for tea plant germplasm resources was explored. Stepwise Fisher discriminant analysis was used to identify the 61 tea plant germplasm resources, achieving an accuracy of 93.44 % with the discrimination accuracy of each grade is above 90 %.

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