Optimizing soil carbon content is essential for mitigating climate change. Understanding the soil organic carbon (SOC) contents and their relationship with plantation age is important for enhancing SOC in tea plantations. However, there is still a lack of studies in quantifying the SOC–age curve of the whole life cycle in tea plantations. Thus, in this study, we collected 140 soil samples aged 3 to 60 years in the four representative regions (Anxi, Datian, Qingliu, and Fuzhou) in Fujian Province to quantify the SOC contents and their relationship with plantation age. We found that the average SOC was 14.6 ± 6.1 g/kg in the four sampling regions. Nitrogen (N) emerged as having a highly significant positive correlation with SOC (R² = 0.9). We also found a significant negative correlation between SOC and mean annual temperature (MAT) (R² = 0.6), and a significant positive correlation with mean annual precipitation (MAP) (R² = 0.6). The SOC increased with plantation age at 3–20 years old and peaked at 16–20 years old. After 35 years, the SOC decreased gradually with the aging of the plantation. The results indicated that tea plantations could be renewed after the age of 35. These results showed that optimizing age structure is important in enhancing SOC in tea plantations and is meaningful in achieving carbon neutrality.

Bactrocera dorsalis (Hendel, 1912) is a highly destructive pest affecting fruits and vegetables, making population monitoring essential for farmers to implement timely control measures. In recent years, although automatic monitoring systems for B. dorsalis have been introduced, challenges such as limited accuracy, difficulty in accurately identifying the target pest using infrared interruption sensors alone, and high labor requirements persist. This study presents an automatic monitoring system consisting of intelligent bait equipment (IBE), an advanced detection model based on YOLOv8, and an online monitoring platform. The developed IBE is equipped with cameras, attractant-based lures, and an automatic removal mechanism for B. dorsalis. Field tests demonstrated the IBE exhibited an attractiveness to B. dorsalis comparable to conventional traps, achieved a near-perfect cleaning efficiency (~100%), and maintained a reliable wireless transmission system. The YOLOv8l-based automatic pest detection model outperformed other YOLOv8 variants (n, s, m, x), achieving the highest precision (95.17%), recall (94.15%) and F1 score (94.66%), underscoring its effectiveness in pest detection. Further analysis of the impact of B. dorsalis density on YOLOv8l’s detection performance revealed a decline in accuracy as density increased; however, even at high densities, the model maintained a strong F1 score of 93.36%, demonstrating robustness. Finally, the automatic pest detection model was integrated into ‘YunShanPu’, an online platform for real-time pest monitoring. The proposed method has demonstrated promising performance in the automatic identification and counting of B. dorsalis and has potential for monitoring B. dorsalis populations continuously, providing early warning and forecasting for integrated pest management.