Machine Learning Applications in Smart Agricultural Farming, Opportunities, Challenges and Future Trends

Abstract

The purpose of the smart agricultural farming is to enhance the optimization of the scarce lands and other agricultural resources to improve food security. Smart agricultural farming employs Internet of Things (AI) technologies/devices to create automated and sustainable farming. The AI devices such as sensors, actuators and other smart agricultural devices generate huge amount of data.  Therefore, the huge amounts of the data generate in the smart agricultural farming, are subjected to analytics, for new knowledge discovery and gain insight, for decision making. Recently, application of the machine learning for smart agricultural farming has gained special attention from research community. However, despite the popularity, special attention gained and achievements made by the machine learning in solving problems in smart agricultural farming, no review or survey has been dedicated mainly on the application of machine learning in smart agricultural farming, to show the opportunities, challenges and future trends. Therefore, in order to bridge this gap, this study conducted a dedicated on survey study on the machine learning applications in smart agricultural farming. In this study, opportunities, challenges and future trends on the applications of machine learning in smart agricultural farming have been unveiled. They study provides opportunities to research community and other experts to propose novel approaches and framework for developing research areas on the machine learning applications in smart agricultural farming. Moreover, new researchers interested in the machine learning applications in smart agricultural farming, can use this study as an entry point.