Predicting opinion using deep learning: From burning to sustainable management of organic waste in Indian State of Punjab

Abstract

In winter season, the burning of crop residues for ease of sowing the next crop, along with industrial emissions and vehicular pollution leads to settling of a thick layer of smog in northern part of India. Therefore, to understand the opinion of farmers regarding sustainable management of organic waste, the present study was conducted in Ludhiana district of Indian state of Punjab. An ex post facto research design was used and a total of 800 dairy farmers having significant crop area were selected randomly for the study, grouped equally as small and large dairy farmers. Results revealed that majority of farmers had a highly favourable opinion regarding organic waste management due to the fact that they were aware of the ill-effects of undesirable practices like crop residue burning. Further, to predict the farmers’ opinion and the effect of independent variables on farmers’ opinion, a multi-layer perceptron feed-forward deep neural network was developed with mean squared error of 0.036 and 0.137 for validation and training data sets respectively, marking a novel approach of analysing farmers’ behaviour. The neural network highlighted that with increase in the magnitude of input variables, namely, education, experience in dairying, information source utilisation, knowledge regarding organic waste management, etc., the farmers’ opinion regarding sustainable waste management increases. The study concluded with the impression that cognitive processes like education, information and knowledge play a significant role in forming the opinion of the farmers. Therefore, efforts focusing on enhancing cognition should be made for sustainable management of organic waste.