水稻稻瘟病病害程度预测模型数据

水稻稻瘟病病害程度预测模型数据,输入为降水量,干球温度,相对湿度,风速,输出为稻瘟病灾害程度。该模型帮助解决了水稻稻瘟病病害程度与象因素的关系建模的问题，有助于农业病虫害预防。水稻稻瘟病病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水稻稻瘟病病害情况及水稻稻瘟病病害的发病规律。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瘟病病害程度。该模型通过输入当天的干球温度,相对湿度,降水量,风速,输出为水稻稻瘟病病害程度。Dataset for rice blast disease severity prediction model. The input features are precipitation, dry-bulb temperature, relative humidity and wind speed, with the output being the severity of rice blast disaster. This model addresses the challenge of modeling the correlation between rice blast disease severity and meteorological factors, supporting the prevention of agricultural pests and diseases. The model for the relationship between rice blast disease severity and meteorological factors first collects rice blast disease cases and the disease incidence regularity by consulting a large volume of literature and integrating expert experience. Leveraging backpropagation (BP) neural network algorithm, the model employs historical disease surveillance data and matched meteorological data to forecast the future severity of rice blast disease. Given daily dry-bulb temperature, relative humidity, precipitation and wind speed as inputs, the model outputs the severity of rice blast disease.

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特点

该数据集为水稻稻瘟病病害程度预测模型数据，包含778396条记录，每周更新，用于预测稻瘟病灾害程度。模型基于BP神经网络算法，输入气象数据（如降水量、温度等），输出病害程度预测结果。