Research On Corn Yield Prediction Model Based On Improved Deep Stacked LSTM Network

In recent years,with the development of computer technology and the improvement of agricultural production level,the combination of agricultural production and data statistics has become more and more closely.With the support of a large number of agricultural data and high-speed computers,statistical models and neural networks are used to analyze the growth process of crops.and production forecasts are gradually being realized.When predicting crop yield,it is necessary to collect data on factors affecting crop yield.Although there are many influencing factors,in many cases,researchers can only obtain a small part of them.Meteorological information is the main input object.Considering the amount of data,this paper chooses to predict crop yields at the county level.It only needs to select data from a certain year and enough counties to start the prediction work.Therefore,we choose to add the longitudes of different counties.,latitude and year are distinguished as supplementary input information.This paper selects corn as a crop for yield prediction.In the current corn yield research using meteorological information as the main input,most researchers directly input all information into the network for feature extraction without distinction.In doing so,there will be the following Two problems:（1）The intrinsic connection between the input information cannot be extracted;（2）The importance of different input information to the final output cannot be distinguished.In order to solve the above problems,this paper chooses to collect the meteorological information features in monthly units,sort these meteorological information features in monthly units according to the order of the growth cycle of corn,and divide them by month as a time step to form a composition ranging from sowing month to k time step vectors of harvest months,the dimension of a single time step vector is the number of meteorological factors collected,and k is the number of months in the growth period.According to the above content,this paper firstly proposes the NPAM-LSTM（No-Parameter Attention Mechanism-Long Short-Term Memory）network to distinguish the importance of meteorological information features,and uses the NPAM part of the network to extract the impact of each input meteorological factor on corn yield.The importance of the prediction results,combined with the LSTM network,uses NPAM again to extract features from the time series divided by months as time steps,and finally output the prediction results.This paper also proposes PFSF-DSN（Primary Factors and Secondary Factors-Deep Stacked Networks）based on the NPAM-LSTM network to more accurately predict corn yield.PFSF-DSN constructs two deep stacking networks,forward and reverse,to extract information from the forward and reverse time series directions,respectively.Each deep stacked network consists of PFSF-LSTM（Primary Factors and Secondary Factors-Long Short-Term Memory）and multiple layers of LSTM.The PFSF-LSTM network is partly composed of the time series composed of the main meteorological factors and the time series composed of the secondary meteorological factors as input.This paper compares the corn yield prediction results of the proposed PFSF-DSN and Ridge,LASSO,RF,LSTM,TCN,NPAM-LSTM and other models based on the same meteorological information and some supplementary information.In performance,PFSF-DSN outperforms other comparison models.In addition,this paper also designs ablation experiments to prove the rationality of the model improvement.