A deep neural network approach for intelligent crop selection and yield prediction based on 46 parameters for agricultural zone-28 in Bangladesh

Abstract

Agriculture is the essential ingredients to mankind which is a major source of livelihood and that provides the wide-reaching scope of working opportunities for rural people in underdeveloped or developing countries. Agriculture work in Bangladesh mostly done with old ways which directly affects our economy. In addition to, institutions of agriculture are working with manual data which cannot provide a proper solution for crop selection and yield prediction. The contribution of our thesis is to achieve the best crop selection and yield prediction in minimum cost and effort. Artificial Neural Network considered as robust tools for modeling and prediction. This algorithm aims to get better output and prediction. As well as, support vector machine, Logistic Regression, and random forest algorithm are also being considered in this thesis for comparing the accuracy and error rate. Moreover, all of these algorithms used here just to see how well they performed for a dataset which is over 0.3 million. We have collected 46 features such as – maximum and minimum temperature, average rainfall, types of land, types of chemical fertilizer, types of soil, soil moisture,soil moisture, soil consistency, soil reaction and soil texture and created our dataset for applying into this prediction process. The dataset we have considered are from past ten years (2008-2017) of Bangladesh. Therefore, based on this parameter we will predict the best possible crop selection and yield prediction intelligently.