A Deep Neural Network Approach for Crop Selection and Yield Prediction in Bangladesh
This work addresses the problem of inefficient agricultural practices and manual data handling for farmers and institutions in Bangladesh, representing an incremental application of existing methods to a specific domain.
The paper tackled crop selection and yield prediction in Bangladesh by applying deep neural networks and other algorithms to a dataset of over 0.3 million records with 46 parameters, achieving improved accuracy and reduced error rates compared to methods like support vector machine and logistic regression.
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.