Integrating field images and microclimate data to realize multi-day ahead forecasting of maize crop coverage using CNN-LSTM  

作　　者：Xin Wang Yu Yang Xin Zhao Min Huang Qibing Zhu 

机构地区：[1]Key Laboratory of Advanced Process Control for Light Industry,Ministry of Education,Jiangnan University,Wuxi 214122,Jiangsu,China [2]School of Physics and Electronic Electrical Engineering,Huaiyin Normal University,Huai’an,223300,Jiangsu,China

出　　处：《International Journal of Agricultural and Biological Engineering》2023年第2期199-206,共8页国际农业与生物工程学报（英文）

基　　金：financially supported by the National Natural Science Foundation of China(Grant No.61772240;No.51961125102);the 111 Project(B12018).

摘　　要：Crop coverage(CC)is an important parameter to represent crop growth characteristics,and the ahead forecasting of CC is helpful to track crop growth trends and guide agricultural management decisions.In this study,a novel CNN-LSTM model that combined the advantages of convolutional neural network(CNN)in feature extraction and long short-term memory(LSTM)in time series processing was proposed for multi-day ahead forecasting of maize CC.Considering the influence of climate change on maize growth,five microclimatic factors were combined with historical maize CC estimated from field images as the input variables of the forecasting model.The field experimental data of four observation points for more than three years were used to evaluate the performance of CNN-LSTM at the forecasting horizon of three to seven days ahead and compared the forecasting results to CNN and LSTM.The results demonstrated that CNN-LSTM obtained the lowest RMSE and the highest R2 at all forecasting horizons.Subsequently,the performance of CNN-LSTM under univariate(historical maize CC)and multivariate(historical maize CC+microclimatic factors)input was compared,and the results indicated that additional microclimatic factors were effective in improving the forecasting performance.Furthermore,the 3-day ahead forecasting results of CNN-LSTM in different growth stages of maize were also analyzed,and the results showed that the highest forecasting accuracy was obtained in the seven leaves stage.Therefore,CNN-LSTM can be considered a useful tool to forecast maize CC.

关 键 词：maize crop coverage multi-day ahead forecasting CNN-LSTM field images microclimatic factors 

分 类 号：S513[农业科学—作物学]