Abstract
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 ( R 2 ) between the measured chlorophyll concentration and the predicated chlorophyll concentration was 0.9014, and the root mean square error (RMSE) was 2.58.
Published Version
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