水稻作物产量预测模型的研究——基于多源数据回归模型  

A Study of Rice Crop Yield Prediction Model—Based on Multi-source Data Regression Model

作　　者：李松涛[1] Li Songtao(Hanzhong Vocational and Technical College,Hanzhong 723002,China)

机构地区：[1]汉中职业技术学院,陕西汉中723002

出　　处：《农机化研究》2024年第10期27-31,36,共6页Journal of Agricultural Mechanization Research

基　　金：陕西省重点研发计划(一般-农业领域)项目(2019NY-041)。

摘　　要：水稻是我国重要的粮食作物之一,在我国不同农业生产区域都有种植,实现水稻产量的精准预测对于稳定我国粮食安全具有重要意义。为了改良传统产量预测方法,实现水稻产量高效预测,基于主成分分析法提取主成分,再将主成分作为BP神经网络模型的输入,对水稻种植面积较大的黑龙江省、江苏省、湖南省和湖北省2011-2020年的数据进行预测分析。研究结果表明:水稻产量与月最高土壤温度、月最低土壤温度、月土壤平均温度、月大气最高温度、月大气平均温度、月平均大气湿度为极显著相关,与月降雨量显著相关,与月大气最低温度相关性较弱。主成分分析与BP神经网络组合模型下,水稻产量的预测精度明显高于传统BP神经网络模型,R^(2)达到0.86,MAPE仅为0.97%,RMSE为0.93,预测值与试验值之间拟合程度较高,模型验证结果表明模型预测结果准确稳定。研究结果对于更加科学、合理地预测水稻产量具有重要的指导意义。Rice is one of the important food crops in China and is grown in different agricultural production regions in China,and it is important to achieve accurate prediction of rice yield to stabilize food security in China.In order to improve the traditional yield prediction method and achieve efficient rice yield prediction,this study firstly extracts principal components based on principal component analysis,and then uses the principal components as the input of BP neural network model to predict and analyze the data from 2011-2020 in Heilongjiang Province,Jiangsu Province,Hunan Province and Hubei Province,which have large rice cultivation areas.The results showed that rice yield was highly significantly correlated with monthly maximum soil temperature,monthly minimum soil temperature,monthly average soil temperature,monthly maximum atmospheric temperature,monthly average atmospheric temperature,monthly average atmospheric humidity,significantly correlated with monthly rainfall,and weakly correlated with monthly minimum atmospheric temperature.The prediction accuracy of rice yield under the combined model of principal component analysis and BP neural network was significantly higher than that of the traditional BP neural network model,with R 2 reaching 0.86,MAPE only 0.95%,and RMSE 1.13.The degree of fit between the predicted and experimental values was high,and the model validation results showed that the model prediction results were accurate and stable.The model validation results showed that the model prediction results were accurate and stable.The research results have important guiding significance for more scientific and reasonable rice yield prediction.

关 键 词：水稻 产量预测 多源数据 BP神经网络 主成分分析 

分 类 号：S11.7[农业科学—农业基础科学] S126