【目的】 小贯小绿叶蝉Empoasca onukii是在全国各大茶区均严重危害的一种害虫，建立适用于各地的预测方法并构建预警信息系统，可为茶小绿叶蝉的测报技术标准化和及时防控提供技术支撑。【方法】 通过长期系统监测全国四大茶区代表性茶园的叶蝉发生动态，基于2011-2017年茶小绿叶蝉的发生动态和气候数据，利用模糊综合评价法建立全国四大茶区叶蝉第一峰发生程度预测方法，并基于Web构建了茶树病虫害监测预警平台。【结果】 本研究掌握了茶小绿叶蝉近年在4个代表茶区的发生规律，其发生动态曲线以单峰型和双峰型为主，第一发生高峰期主要集中在5-7月，是全年的主要危害期。通过筛选影响发生的越冬虫口量和气象数据关键因子（2月平均气温和上年12月至2月最低气温之和），构建出叶蝉第一峰发生程度预测方法和预警平台，进一步利用2018-2022年的叶蝉实际发生程度对预测方法的应用效果进行外延检测，总体测报准确率达到83.64%。【结论】 建立的茶小绿叶蝉第一峰发生程度预警方法准确率较高，利用预警平台可实现监测数据信息分析、传递、查询共享以及预警发布一体化等综合服务功能，茶叶管理部门、农技人员可以在茶小绿叶蝉的预测预报工作中推广应用。

 [Aim]  To develop an accurate prediction method suitable for local conditions and thereby improve the prevention and control of the tea green leafhopper (Empoasca onukii), a major pest of tea in all tea-growing regions of China. [Methods]  We conducted long-term investigations of leafhopper density in representative tea gardens in four major regions. Based on climate data and the population dynamics of the species (2011 to 2017), we used a fuzzy comprehensive evaluation method to develop a prediction method for the first population peak of leafhoppers in each region. A Web-based monitoring and early warning platform for tea plant diseases and insect pests was also established. [Results]  This study identified the occurrence patterns of tea green leafhoppers in four representative tea-growing areas from 2011 to 2017. This species generally had either single, or double, peaks of abundance. The first peak was usually between May and July, which was the most severe period of crop damage in a year. By identifying key factors influencing the overwintering population, and analyzing meteorological data (specifically, the average February temperature and the sum of the lowest temperatures from December to February), we developed both a prediction method and an early warning platform for the first peak of abundance of this pest. Testing this method with actual leafhopper abundance data from 2018 to 2022 indicates that it has an overall accuracy rate of 83.64%. [Conclusion]  The new early warning method has high accuracy and the early warning platform provides various services, including data analysis, data transmission, query sharing, and early warning of peak abundance. Tea management departments and agricultural technicians can promote the use of this method for predicting and forecasting peak abundance of the tea green leafhopper.