基于幼果期气象数据苹果产量早期预测研究——以运城市为例

本文通过对运城市2005—2018年气象数据和苹果年产量数据进行分析,构建运城市苹果产量早期预测模型。首先,采用HP滤波法将运城市苹果年产量分为趋势产量和气象产量。其次,分别对苹果物候期：发芽期、花期、幼果期、膨果期、成熟期建立多元线性回归模型,研究每个物候期对苹果气象产量影响的强弱。最后,选取对苹果气象产量影响最强的幼果期建立BP神经网络早期预测模型,并对其进行验证。结果表明：选取幼果期建立的BP神经网络苹果产量早期预测模型其预测结果相对平均误差为7.08%,使用2019年相关数据验证BP神经网络产量早期预测模型的精度为89.6%,表明该模型能够较为准确的预测苹果产量,可为农作物产量早期预测提供理论支持。

Early Prediction of Apple Yield Based on Meteorological Data in Young Fruit Stage:An Example of Yuncheng City

By analyzing the meteorological data and apple annual yield data in Yuncheng City from 2005 to 2018, an early prediction model of apple yield was constructed. Firstly, the annual yield of apple was divided into trend yield and meteorological yield by using HP filtering method. Secondly, multiple linear regression models were established respectively for apple phenology stages: germination stage, flowering stage, young fruit stage, expanding stage and mature stage, to study the influence of each phenology period on apple meteorological yield. Finally, the early prediction model of BP neural network was established and verified in terms of the young fruit stage which had the strongest influence on meteorological yield of apple. The relative average error of early yield prediction model based on BP neural network was 7.08%, and the accuracy of BP neural network early prediction model was verified with the relevant data in 2019, which was 89.6%. The model could accurately predict apple yield and provide theoretical support for early crop yield prediction.