Agricultural yield and profit prediction using data analysis techniques

Abstract

This research proposes a model that will enable us to predict the agricultural outcome of a country. The prediction will include the agricultural yield for the upcoming years as well as predict the profit margin for particular crops. Although some researches have been done in this field but most of those are in Bangladesh perspective. In our country, being the oldest form of raising economy, agriculture is a field that has not yet been blessed by modern technology or data analysis. This prediction is made by analyzing the dataset of certain vital parameters for rice production, such as Temperature, Humidity, Sunshine and Area using different Regression Analysis and Support Vector Machine (SVM) techniques. The dataset containing the history of Rice Production, Price Diversity and Fertilizer Usage, taken from the yearbook of Bangladesh Agricultural Development Corporation (BADC), Bangladesh Rice Research Institute (BRRI), Ministry of Agriculture (Bangladesh) and by some primary data collection from Gazipur and Pabna, is also analyzed to calculate the profit per year along with comparing the accuracy of the prediction. This proposed model aims to pave the way for data science to touch the sector that keeps our economy running aiming to maximize the production of crops which will result in more profit for the farmers as well as contribute to the economy with the ancient form of revenue collection.