Predicting the Rice Production of Bangladesh by Machine Learning Technique

Abstract

Bangladesh is an agricultural country and its economic condition largely depends on agriculture. The country produces much agricultural merchandise like rice, jute, wheat, onion, chilly, banana, garlic, ginger, pulse and so on. However, rice (Oryza Sativa) is produced most widely all over the country. Moreover, United States Department of Agriculture (USDA), 2017 estimate that the rice production of Bangladesh is 34.7 million metric tons for the period 2017–2018 and the position of Bangladesh is after China, India, and Indonesia. In addition, Bangladesh produces three types of rice which are Aus, Aman, and Boro. A huge portion of the population in Bangladesh immensely depends on rice as the main food. So, this paper attempts to predict the rice production of Bangladesh with the help of machine learning model like Artificial Neural Network (ANN). This paper considers a secondary data set of yearly rice production in Bangladesh over the period 1971–1972 to 2014–2015. This paper identifies the most suitable neural network model with architecture ANN 3*3*2*1 based on model selection criteria like MSE. Thus, this paper suggests an ANN model for envisaging the rice production of Bangladesh.

Keywords: Machine learning, prediction, rice prediction, neural network

Cite this Article

Shohel Mahmud. Predicting the Rice Production of Bangladesh by Machine Learning Technique. Research & Reviews: Journal of Agricultural Science and Technology. 2018; 7(3): 7–13p.