Integrating IoT and Edge Computing for Smart Agriculture with Real-Time Data Analytics

This phenomenon of smart agriculture revolution occurs on the basis of the integration of IoT and edge computing since it enables real-time analysis to help make critical decisions along with resources and productivity enhancement. The existing agriculture scenario relies on cloud computing in the centralized data centers but they cause delays and reduce the number of bandwidths. Data in edge computing is processed locally close to their source due to which they offer quick response time and reinforce the performance of the system. IoT such as sensors combined with drones and automatic irrigation equipment provide real time information on agricultural conditions related to soil moisture and temperature along with humidity and plant condition. When on-site edge computing solutions are installed, naive farms are used as collection points and can conduct timely analysis and allow responsiveness in real-time allowing specific irrigation and removal of pests. Farmers have the capability of predicting with real time analytics to perform maintenance, identify diseases in its early stages and improve yield estimates that enhances sustainability and efficiency. Operational systems involving machine learning algorithms improve the decision-making process since they are able to identify the patterns along with the anomalies that are emerging. The study focuses on the applications of IoT and edge computing into intelligent agriculture with the evaluation of its architecture and showing the benefits of its operation, such as its efficiencies, as well as the scalability of its system and lower energy consumptions. This research gives the practical implementation of the high-tech computing practices and gives possible ways of further development of technology to allow maintenance of technology oriented way of farming.