Study of IOT Framework in Agriculture Field for Enhancing Production of Crop with Multivariate Sensors
Massive scaling in the Internet of things (IoT) is very important so that it provides information gathered from the sensor of multiple heterogeneous sources in a network. In recent trends, Smart agriculture predicts data analysis with combination of IoT and wireless sensor network (WSN). It is in demand for more food production to increase yields, optimized resource utilization, smart irrigation control, cost effective system. A main focus is to fulfill the human necessity demand. The above goal is achieve by applying deep learning method on IoT framework of agriculture field. Which recommend us to provide best yield of the efficient crops as well as prediction of medicines essential for crop diseases. Monitoring through real time data from multivariate sensors to maximize food production will give more innovative solution in agriculture field. Because multivariate sensors can monitor all the variables in the same time. Due to which early detection of changes can be utilized in prediction system. In this paper result of a systematic literature review focus on real time energy efficient model with multivariate sensors and focus on accurate prediction results.