Architecture Design of a Smart Farm System Based on Big Data Appliance Machine Learning

The size of the world's population increased at a Revolution. The modern expansion of human numbers started but environmental degradation with lack of urban services. To satisfy the growing of human food, worldwide demand for grain the area under production should be increased, and productivity must be improved on yields area firstly. To evaluate the Smart Farming sub-use cases' overall outcome, each economic and environmental benefits, social aspects, and the technical evolution path were evaluated. We have like an significant improvement in the economic outcome of the farm. This paper proposed an implementation of BMS (Big Data Application Machine Learning-based Smart Farm System) with an emphasis on crop productivity and the importance of farmers' income increase. Increasing crop productivity is also important to increase essentials' income, enhance farmer field-level insights, and actionable knowledge to produce when the crop is of the best quality or selling it with a good price. Therefore, in the Smart Farm system proposed in this paper specially in case of big data science, we need to consider data analysis and machine learning as the most important steps and then we can include the value of big data science. Machine learning is an essential ability to learn from data and provide data-driven information, decisions, and forecasts. Traditional approaches to machine learning were developed in a different era, like the data set that fully integrates memory. In addition to the characteristics of Big Data, they create obstacles to traditional techniques. One of the objectives of this document is to summarize the challenges of machine learning with Big Data.