Prediction of the Effect of Nutrients on Plant Parameters of Rice by Artificial Neural Network

Rice holds key importance in food and nutritional security across the globe. Nutrient management involving rice has been a matter of interest for a long time owing to the unique production environment of rice. In this research, an artificial neural network-based prediction model was developed to understand the role of individual nutrients (N, P, K, Zn, and S) on different plant parameters (plant height, tiller number, dry matter production, leaf area index, grain yield, and straw yield) of rice. A feed-forward neural network with back-propagation training was developed using the neural network (nnet) toolbox available in Matlab. For the training of the model, data obtained from two consecutive crop seasons over two years (a total of four crops of rice)…