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.