水晶梨病虫害防治预测模型

Study on prediction model of disease and pest control of crystal pear

【目的】为提高水晶梨病虫害防治工作效率,进一步提升病虫害的预测效果和精度。【方法】深入研究了灰色模型(GM),利用GM对水晶梨环境因子数据进行建模得到病虫害预测公式,通过差分方程推导出时间响应式和参数估计,建立了优化初始值的灰色模型(OIVGM),将OIVGM与BP神经网络预测模型(BP)进行组合,建立了优化初始值的灰色BP神经网络预测组合模型(OIVGM-BP)。【结果】通过单位根检验法测量模型的稳定性,OIVGM-BP一阶差分处理后,T统计量(-5.487654)小于5%临界值(-2.878073),数据序列表明平稳,OIVGM-BP可以稳定进行预测。通过白噪声检验方法测量OIVGM-BP的适应性,OIVGM-BP的残差P值从第二阶开始,均大于0.05,说明OIVGM-BP的适应性较好,各阶均通过了白噪声检验。LRM、GM、TSM、BP、OIVGM-BP对梨锈病、白粉病、腐烂病、梨黄粉蚜、梨二叉蚜、梨木虱6种病虫害的预测准确率的平均值分别为70.81%、70.09%、69.74%、65.64%、83.01%,OIVGM-BP的预测准确率优于其他4种预测模型。【结论】OIVGM-BP能够对水晶梨病虫害进行有效预测,能够更好地指导农业生产。

【Aim】This study was conducted to improve the efficiency of the pest control used for crystal pears,and to build a prediction model to determine the effecctiveness of treatments against diseases and pests.【Method】The grey model(GM)was used to model environmental factors relevant for crystal pears to obtain a pest forecast.The time response formula and parameter estimation were derived through the differential equation,and a grey model(OIVGM)for optimizing the initial value was established.The OIVGM was combined with the BP neural network prediction model(BP),and this grey BP neural network prediction combination model(OIVGM-BP)was used to optimize the initial value.【Result】In this paper,the stability of the model is measured by the unit root test.After the first-order difference processing of OIVGM-BP,the T statistic(-5.487654)is less than the 5%critical value(-2.878073).The data series is stable indicate that OIVGM-BP can predict stably.This paper measures the adaptability of OIVGM-BP by using the white noise test method.The P value of the residual of OIVGM-BP starts from the second order and is greater than 0.05,indicating that the adaptability of OIVGM-BP is good,and each order has passed the white noise test.The experimental results show that the average prediction accuracy of LRM,GM,TSM,BP and OIVGM-BP for six diseases and insect pests of pear rust,powdery mildew,rot,pear yellow aphid,pear binary aphid and pear wood lice are 70.81%,70.09%,69.74%,65.64%and 83.01%respectively,the prediction accuracy of OIVGM-BP is better than the other four classical prediction models.【Conclusion】OIVGM-BP can effectively predict diseases and insect/pest infestations in crystal pears and guide agricultural production.