Design and Development of an Efficient Agriculture Management System in Cloud Computing using Machine Learning

Major economies like those of India, China, and the United States are said to be based on agriculture. Achieving higher industrial growth in these nations will be made possible by good agricultural production. To improve agriculture productivity, farmers must receive timely information. In order to collect data on the wind, temperature, humidity level, and other variables, several types of sensors were used by the remote sensing and geographic information system (GIS) (RSF) technologies. This data is then saved in a cloud computing platform. Machine learning (ML) techniques will be used to examine the data and forecast weather information for farmers. This study gives a thorough analysis of several existing agricultural management systems and a machine learning (ML) model for improving farm management systems (AMS). According to the report, processing and storing data on a cloud platform requires more computing than the current access control and machine learning models. In order to construct scalable and economical agriculture management systems using ML and cloud computing environments, this study proposes an Efficient Agriculture Management System.