Predictive data using linear regression in agricultural production

In agribusiness some challenges are related to generating information for predictability with an acceptable safety accuracy. In this context, data management systems are usually developed to meet only the operational, legal, and regulatory requirements. The gap in functionalities regarding data science creates the opportunity to develop complementary tools such as business intelligence, data warehousing, online analytics, and others. This paper presents an approach to predict possible scenarios from historical harvested crops datasets. We conducted our proposal using a set of government data on harvests in all regions of Brazil in a historical series of 45 years. We have developed a descriptive application for predictive data analysis and information generation for forecasting scenarios in agriculture, using machine learning with a predictive algorithm implemented with linear regression. Objectively, the results show the use of real datasets to generate possible values in crops according to previous seasons.