  Agriculture is the essential ingredients to mankind which is a major source
of livelihood. Agriculture work in Bangladesh is mostly done in old ways which
directly affects our economy. In addition, institutions of agriculture are
working with manual data which cannot provide a proper solution for crop
selection and yield prediction. This paper shows the best way of crop selection
and yield prediction in minimum cost and effort. Artificial Neural Network is
considered 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 is also considered in this study for
comparing the accuracy and error rate. Moreover, all of these algorithms used
here are just to see how well they performed for a dataset which is over 0.3
million. We have collected 46 parameters such as maximum and minimum
temperature, average rainfall, humidity, climate, weather, and types of land,
types of chemical fertilizer, types of soil, soil structure, soil composition,
soil moisture, soil consistency, soil reaction and soil texture for applying
into this prediction process. In this paper, we have suggested using the deep
neural network for agricultural crop selection and yield prediction.
