The major challenge for farmers in agricultural filed is selecting an appropriate crop for planting. Crop selection is depended on several factors like climate, soil nature, market etc. Majorly crop yield production depends on weather conditions and soil types. Yield anticipating is essential for farmers nowadays, which especially adds to the appropriate selection of yield for sowing. There is no framework set up to recommend farmers what type of crops to develop. It is an essential and challenging task to take right farming decisions at a future steady cost and yield balance. In this article, we proposed an Artificial Neural Network (ANN) model for rice crop yield prediction by utilizing weather parameters like rainfall, temperature, sunshine hours and evapotranspiration. Generally, Default-ANN has only one hidden layer but in this work, we designed a Personalized Artificial Neural Network (PANN) approach by varying number of hidden layers, number of neurons and learning rate. P-ANN model accuracy is computed in terms of R-Square (R2) and Percentage Forecast Error (PFE). Outcomes demonstrate that P-ANN model performs precisely with a greater R2 and smaller PFE values compared with existing methods. For this research, we used the seasonal (Kharif & Rabi) weather dataset and rice yield data of Guntur district, Andhra Pradesh, India during the period 1997-2014. Better paddy yield was forecasted by utilizing P-ANN approach.
Volume 11 | 05-Special Issue
Pages: 2209-2219