Abstract

Early and accurate crop yield estimates at a local and national level are essential to oversee industry and trade planning and to mitigate the price hypotheses. The major challenge for farmers in the agricultural field is selecting an appropriate crop for planting. Crop selection is dependent on several factors like climate, soil nature, market, etc. Majorly, crop yield production depends on weather conditions and soil types. Yield anticipation is essential for farmers nowadays, which significantly adds to the appropriate yield selection for sowing. There needs to be a framework to recommend what type of crops to produce for farmers. It is essential and challenging to make the right farming decisions at a future steady cost and yield balance. This article proposes 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, a Personalized Artificial Neural Network (PANN) approach has been designed by varying the number of hidden layers, the number of neurons, and the learning rate. P-ANN model accuracy is computed using R-Square (R2) and Percentage Forecast Error (PFE). Outcomes demonstrate that the P-ANN model performs precisely with a greater R2 and smaller PFE values than existing methods. For this research, the seasonal (Kharif & Rabi) weather dataset and rice yield data of Guntur district, Andhra Pradesh, India, from 1997-2014 have been used. Better paddy yield was forecasted by utilizing the P-ANN approach.