Nitrogen estimation of paddy based on leaf reflectance using Artificial Neural Network

Nitrogen (N) is one of nutrient required by plant in huge amounts. N availability of plant is needed to be estimated before applying fertilizers to determine proper N application rate. The purpose of this study is to estimate N of paddy (Oryza sativa, sp.) based on leaf reflectance using Artificial Neural Network (ANN). In this study, 45 leaf samples were randomly selected under various environmental condition. Leaf reflectance was measured by handheld spectroradiometer while actual leaf N content was determined by Kjeldahl method. Spectral reflectance data in visible band (400–700 nm wavelength region) and actual N content were used as input and target data in ANN model building. K-fold cross-validation (k=3) method was applied to select the best model and measure the overall performance of model. Results indicated that ANN model with 17 neurons of hidden layer in relatively could estimate N properly. It was shown by the lowest root mean square error (RMSE) of 0.23 and the highest prediction accuracy of 93%. This study promises to help farmers predicting N content of paddy for optimal N fertilizer application.