基于离散过程神经元网络的农作物产量预测模型及其应用

A crop yield forecasting model based on discrete process neural networks and its application

采用离散过程神经元网络建立农作物产量基于种子、土壤、施肥、气候等影响因素下的预测模型,使用模拟退火-遗传算法对权值进行分层修订,并通过实际数据进行验证,得出此模型泛化能力较强,可以应用于其它经纬度,其它农作物生长参数的预测中,是一种全新的动态预测方法的结论。

In this article, it established a crop yield prediction model which based on the seeds, soil, fertilization, climate and other factors by the discrete process neural network, after layer-revised the weights using simulated annealing genetic algorithm, and validated by the actual data, it draws a conclusion that the model is a new method of dynamic prediction and generalization capability is stronger, which can be applied to other latitude or crop growth parameters prediction.