Abstract
There are obvious differences in quality between different varieties of the same plant, and it is not clear whether they can be effectively distinguished from each other from a bacterial point of view. In this study, 44 tea tree varieties (Camellia sinensis) were used to analyze the rhizosphere soil bacterial community using high-throughput sequencing technology, and five types of machine deep learning were used for modeling to obtain characteristic microorganisms that can effectively differentiate different varieties, and validation was performed. The relationship between characteristic microorganisms, soil nutrient transformation, and tea quality formation was further analyzed. It was found that 44 tea tree varieties were classified into two groups (group A and group B) and the characteristic bacteria that distinguished them came from 23 genera. Secondly, the content of rhizosphere soil available nutrients (available nitrogen, available phosphorus, and available potassium) and tea quality indexes (tea polyphenols, theanine, and caffeine) was significantly higher in group A than in group B. The classification result based on both was consistent with the above bacteria. This study provides a new insight and research methodology into the main reasons for the formation of quality differences among different varieties of the same plant.