A Novel IoT Framework for Smart Agriculture Using Machine Learning Algorithms

This paper aims to explore the pertinence of future study and usage of IOT in dealing with the use of artificial intelligence in farming as one of the effective ways on how a better practices can be promoted in the farming sector. Consequently, the objective of this paper is to develop a new architecture for IoT usage that can be instituted systematically for monitoring and assessing productivity in the agricultural sector with a view of arriving at the right decision. The framework makes use of the best of what is currently available in machine learning for yield forecasting, the resource for example water and energy, among others. This was, in turn, followed by experiments and simulation and where differential features such as crop yield prediction, resource usage, and amongst others, were enhanced. Some of the suggested ideas have been owning to below 5% accuracy for the crop yield forecasts and lowering the usage of water by a quarter, and accurate detection of the temperature and relative humidity for integration of energy. Thus, one can suppose that the offered framework can be helpful in solving the problems connected with the management and sustainment if the resources for agriculture. For such reasons, the offered framework has a potential to redefine the contemporary agriculture since this framework offers actual advice to farmers.