A Deep Neural Network Approach for Crop Selection and Yield Prediction in Bangladesh

TL;DR

This paper proposes a deep neural network model for crop selection and yield prediction in Bangladesh, comparing its performance with other machine learning algorithms on a large dataset to improve agricultural decision-making.

Contribution

It introduces a deep neural network approach for crop prediction in Bangladesh and compares its accuracy with other algorithms using extensive agricultural data.

Findings

Deep neural network outperforms other algorithms in accuracy

Model effectively predicts crop yield with over 0.3 million data points

Provides a cost-effective solution for agricultural planning

Abstract

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.