Neural-network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices

Heavy metal stress in soils results in subtle changes in leaf chlorophyll concentration, which are related to crop growth and crop yield. Accurate estimation of the chlorophyll concentration of a crop under heavy metal stress is essential for precision crop production. The objective of this paper is to create a back propagation (BP) neural-network model to estimate chlorophyll concentration in rice under heavy metal stress. Three experiment farms located in Changchun, Jilin Province, China with level II pollution, with level I pollution and with safe level were selected, The assessment was based on the input parameters normalised difference vegetation index (NDVI), optimized soil-adjusted vegetation index (OSAVI), modified triangle vegetation index/modified chlorophyll absorption ratio index (MTVI/MCARI), MTVI/OSAVI and the output parameters of rice leaf chlorophyll concentration. The output parameters were sensitive to heavy metal stress. The result indicated that an optimum BP neural-network prediction model has 4-10-2-1 network architecture with gradient descent learning algorithm and an activation function including the sigmoid tangent function in the input layer, a hidden layer and sigmoid logistic functions in the output layer. The correlation coefficient (R2) between the measured chlorophyll concentration and the predicated chlorophyll concentration was 0.9014, and the root mean square error (RMSE) was 2.58.