Yunnan Province, China, known for its superior ecological environment and diverse climate conditions, is home to a rich resource of tea-plant varieties. However, the subtle differences in shape, color and size among the fresh leaves of different tea-plant varieties pose significant challenges for their identification and detection. This study proposes an improved YOLOv8 model based on a dataset of fresh leaves from five tea-plant varieties among Yunnan large-leaf tea trees. Dynamic Upsampling replaces the UpSample module in the original YOLOv8, reducing the data volume in the training process. The Efficient Pyramid Squeeze Attention Network is integrated into the backbone of the YOLOv8 network to boost the network’s capability to handle multi-scale spatial information. To improve model performance and reduce the number of redundant features within the network, a Spatial and Channel Reconstruction Convolution module is introduced. Lastly, Inner-SIoU is adopted to reduce network loss and accelerate the convergence of regression. Experimental results indicate that the improved YOLOv8 model achieves precision, recall and an mAP of 88.4%, 89.9% and 94.8%, representing improvements of 7.1%, 3.9% and 3.4% over the original model. This study’s proposed improved YOLOv8 model not only identifies fresh leaves from different tea-plant varieties but also achieves graded recognition, effectively addressing the issues of strong subjectivity in manual identification detection, the long training time of the traditional deep learning model and high hardware cost. It establishes a robust technical foundation for the intelligent and refined harvesting of tea in Yunnan’s tea gardens.

Flowering and bud dormancy are crucial stages in the life cycle of perennial angiosperms in temperate climates. MADS-box family genes are involved in many plant growth and development processes. Here, we identified three MADS-box genes in tea plant belonging to the FLOWERING LOCUS C (CsFLC) family. We monitored CsFLC1 transcription throughout the year and found that CsFLC1 was expressed at a higher level during the winter bud dormancy and flowering phases. To clarify the function of CsFLC1, we developed transgenic Arabidopsis thaliana plants heterologously expressing 35S::CsFLC1. These lines bolted and bloomed earlier than the WT (Col-0), and the seed germination rate was inversely proportional to the increased CsFLC1 expression level. The RNA-seq of 35S::CsFLC1 transgenic Arabidopsis showed that many genes responding to ageing, flower development and leaf senescence were affected, and phytohormone-related pathways were especially enriched. According to the results of hormone content detection and RNA transcript level analysis, CsFLC1 controls flowering time possibly by regulating SOC1, AGL42, SEP3 and AP3 and hormone signaling, accumulation and metabolism. This is the first time a study has identified FLC-like genes and characterized CsFLC1 in tea plant. Our results suggest that CsFLC1 might play dual roles in flowering and winter bud dormancy and provide new insight into the molecular mechanisms of FLC in tea plants as well as other plant species.