Advances in rice yield estimation using Neural Networks

Abstract

Timely and reliable estimation of crop yield is an important dimension in agriculture as it aids in effective and timely policy decisions. Weather is a the most important factor, affecting crop yield in the agricultural domain and rice crop is no exception. The present study has been taken up to identify the effect of weekly weather parameters namely bright sunshine hours, maximum temperature, minimum temperature, morning relative humidity, evening relative humidity, and weekly total rainfall on rice crop yield being an important staple food of India. In order to suggest a suitable neural network model for rice yield estimation, Ranga Reddy District of Telangana state was chosen and weekly averages of weather variables from the 30th to 47th meteorological standard weeks (MSWs) of 31 years and rice yield data from 1988-89 to 2018-19 were considered in the study. Back A back propagation neural network and two activation functions namely logistic sigmoid and linear were employed in the neural network model. The proposed neural network model “F” (Input Neurons =11, Hidden Neurons=12, Output Neuron=1, Train Data Size = 80 % and Test data Size=20%) exhibited better results with the low MAE and AEER% while estimating rice yields. All the estimated yields of respective years were close to the actual yields as the multiple correlation coefficients (R) values for train and test data were also close to 1. The errors of simulated estimation of rice yield ranged between -8.1 to -3.8 % for the proposed neural networks model. Thus, better rice yield was estimated by using the proposed neural network model “F”.