水稻稻曲病病害程度预测模型数据

水稻稻曲病病害程度预测,输入为降水量、干球温度、相对湿度,风速,输出为水稻稻曲病病害程度预测,该模型帮助解决了水稻稻曲病病害程度预测和与气象因素的关系建模的问题。水稻稻曲病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水稻稻曲病的病害情况及水稻稻曲病的发病规律。模型通过BP神经网络算法，使用历史测报的病害数据及对应的气象数据，可以预测未来的水稻稻曲病的病害情况。该模型通过输入当天风速、相对湿度、干球温度及降水量，来输出预测的未来的病害程度。

Rice False Smut Disease Severity Prediction: The input features include precipitation, dry-bulb temperature, relative humidity, and wind speed, while the output is the predicted severity of rice false smut disease. This model addresses two core tasks: rice false smut disease severity prediction and the modeling of the relationship between the disease and meteorological factors. To establish the relationship model between rice false smut and meteorological factors, we first consulted a large number of academic literatures and integrated expert experience to obtain the disease status and incidence regularities of rice false smut. Leveraging the backpropagation (BP) neural network algorithm, the model uses historical surveyed and forecasted disease data and corresponding meteorological data to predict the future disease status of rice false smut. Specifically, this model takes the daily wind speed, relative humidity, dry-bulb temperature and precipitation as inputs, and outputs the predicted future disease severity.

搜集汇总

数据集介绍

特点

该数据集用于水稻稻曲病病害程度预测，包含593965条企业数据，每周更新。通过BP神经网络算法，结合气象因素（如降水量、温度、湿度和风速）预测病害程度，适用于农业病害预测和气象关系建模。