Monitoring maize growth on the North China Plain using a hybrid genetic algorithm-based back-propagation neural network model

Abstract

Crop growth and early yield information is crucial for the establishment and adjustment of agricultural management plans. Timely, precise and regional assessments of crop growth conditions and production greatly benefit the national economy and agriculture. In this study, remotely sensed leaf area index (LAI) and vegetation temperature condition index (VTCI) data retrieved from the Global LAnd Surface Satellite (GLASS) and Moderate-resolution Imaging Spectroradiometer (MODIS) data were selected as the key indicators of maize growth and a hybrid genetic algorithm (GA)-based back-propagation neural network (BPNN) (GA-BPNN) model was applied to provide complementary information on maize growth at the main growth stage. GA-BPNN models with different architectures were established, and an architecture with 10, 9 and 1 nodes in the input, hidden and output layers, respectively, achieved the best training and testing performance. The root mean square error (RMSE) values of the training and testing samples were 588.2 kg/ha and 663.4 kg/ha, respectively. Thus, the hybrid model with the best architecture (10-9-1) was selected to calculate the values of the integrated growth monitoring index (G) at the regional scale with a 1 km spatial resolution in the study area from 2010 to 2018. The results showed that the monitored maize growth well reflected the actual situation and the correlations between G values and sites’ measured variables, such as the maize yield and soil relative humidity, were higher than that of a pure BPNN model. The linear relationship between the GA-BPNN-based G values and maize yields was analyzed to estimate the maize yield in the North China Plain. Most of the RMSE and mean absolute percentage error (MAPE) values between the estimated and actual maize yields were less than 700.0 kg/ha and 10.0%, respectively. Considering that the estimation errors of most statistical samples were small and there were no obvious differences between the estimated maize yields in the adjacent regions of the northern, central and southern plain, the GA-BPNN-based yield estimation model provided reliable and spatially continuous estimates of maize yield.