Towards applicability of machine learning techniques in agriculture and energy sector

Abstract

Machine learning includes wide range of algorithms for learning predictive rules from historical data and to build a model that can predict unseen future data. As a result, machine learning analyzes data samples to find patterns and create decision rules for developing a predictive model that can be used to forecast future data. A contemporary agricultural paradigm known as smart agriculture examines the entire farm as a collection of small units and finds abnormalities in output and demand for those units. The ultimate goal of smart agriculture is to reduce agricultural costs in order to increase profit. Smart farmers employ cutting-edge agricultural techniques. The predictive nature of machine learning algorithms enables smart farming. Wind speed prediction is necessary to increase the amount of energy produced. Power demand and price forecasting accuracy is regarded as one of the most important research issues in electrical engineering today and in the future. The predictive nature of various machine learning algorithms makes them the best instrument for dealing with energy and power engineering challenges.