山核桃虫害程度预测模型数据

山核桃虫害程度预测,输入为干球温度、相对湿度、降水量、风速,输出为山核桃虫害程度,该模型帮助解决了关山核桃病害程度和与气象因素的关系建模的问题。山核桃虫害程度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山核桃虫害程度及茶尺蠖的生长规律。模型通过多元线性回归法结合BP神经网络，使用历史测报的虫情数据及对应的气象数据，可以预测未来的山核桃虫害程度。该模型通过输入距离当天的平均温度、相对湿度、干球温度、风速，来输出预测的山核桃虫害程度。

Pecan pest infestation degree prediction: The input features include dry-bulb temperature, relative humidity, precipitation, and wind speed, while the output is the pecan pest infestation degree. This model addresses the problem of modeling the relationship between pecan pest infestation degree and meteorological factors.

The model for the correlation between pecan pest infestation degree and meteorological factors first acquires the growth patterns of pecan pests and the tea geometrid (Ectropis obliqua) by consulting a vast body of literature and integrating expert empirical insights. The model combines multiple linear regression and BP neural network, and uses historically collected pest survey data and corresponding meteorological data to predict future pecan pest infestation degrees.

By inputting the average temperature, relative humidity, dry-bulb temperature, and wind speed relative to the current day, this model outputs the predicted pecan pest infestation degree.

搜集汇总

数据集介绍

特点

该数据集主要用于山核桃虫害程度预测，包含592088条数据，每周更新。输入为干球温度、相对湿度、降水量和风速，输出为山核桃虫害程度，采用多元线性回归法和BP神经网络进行预测。