水稻纹枯病预测模型数据

水稻纹枯病病害程度预测,输入数据为降水量、干球温度、相对湿度,风速,输出数据为水稻纹枯病程度,该模型帮助解决了水稻纹枯病预测和与气象因素的关系建模的问题。水稻枯纹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枯纹病的病害情况及水稻枯纹病的发病规律。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枯纹病的病害情况。该模型通过输入当天干球温度、相对湿度、风速及降水量，来输出预测的未来病害程度。Prediction of Rice Sheath Blight Severity. This model takes precipitation, dry-bulb temperature, relative humidity and wind speed as input features, with the output being the severity of rice sheath blight. It addresses the core challenges of rice sheath blight prediction and modeling the relationship between the disease and meteorological factors. To develop the model for the correlation between rice sheath blight and meteorological factors, we first collected disease status and incidence patterns of rice sheath blight by reviewing extensive literature and integrating expert experiences. The model adopts the backpropagation (BP) neural network algorithm, which uses historical disease surveillance data and corresponding meteorological data to predict future rice sheath blight severity. Specifically, by inputting daily dry-bulb temperature, relative humidity, wind speed and precipitation, the model outputs the predicted future disease severity.

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特点

该数据集是一个用于水稻纹枯病预测的模型数据，包含461313条记录，每周更新。它通过BP神经网络算法，利用气象数据（如降水量、温度、湿度和风速）预测水稻纹枯病的病害程度，帮助解决病害预测与气象因素关系建模的问题。