茶叶茶尺蠖虫情预测模型数据

茶叶茶尺蠖虫情预测,输入为降水量、干球温度、相对湿度、风速,输出为茶叶茶尺蠖虫情程度,该模型帮助解决了茶叶茶尺蠖虫情程度的预测和与气象因素的关系建模的问题。茶尺蠖与气象因素的关系模型，首先通过查阅大量文献和对接专家经验获取茶尺蠖的虫情及茶尺蠖的生长规律。模型通过灰色关联分析法，使用历史测报的虫情数据及对应的气象数据，可以预测未来的茶尺蠖的虫害情况。该模型通过输入当天的平均温度、相对湿度、风速及降水量，来输出预测的未来的虫害发生程度。Tea Ectropis obliqua Pest Forecasting Dataset: This model takes precipitation, dry-bulb temperature, relative humidity and wind speed as input features, and outputs the severity level of Tea Ectropis obliqua pest infestation. It addresses the core challenges of forecasting Tea Ectropis obliqua pest severity and modeling the correlation between pest infestation and meteorological factors. To develop the correlation model between Tea Ectropis obliqua and meteorological factors, we first obtained pest occurrence data and growth rules of Tea Ectropis obliqua by reviewing a large volume of literature and integrating expert experiences. Leveraging historical pest monitoring data and corresponding meteorological data, the model adopts Grey Relational Analysis (GRA) to predict future Tea Ectropis obliqua infestation. By inputting the daily average dry-bulb temperature, relative humidity, wind speed and precipitation, the model outputs the predicted severity level of future pest infestation.

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该数据集由杭州五舟长空科技有限公司提供，主要用于茶叶茶尺蠖虫情预测，包含592113条数据，每周更新。数据涉及降水量、干球温度、相对湿度、风速等气象因素，通过灰色关联分析法预测茶尺蠖虫害发生程度。