Ensuring Food Security and Agriculture Demand using Machine Learning and Sensor based Technologies

Abstract

Agriculture is important for sustaining human life. Contribution of agriculture to the Indian economy is around 20%. It is estimated that by the year 2050, Indian will need 60% more food to feed the population of 9.3 billion. However, due to limited resources and land, there is a challenge of food insecurity, need to enhance the efficiency of current farms, plan, smartly grow crops according to the demand to decrease wastage and ensure food security. Technologies like Machine Learning, IoT, Blockchain, Data Analytics, Big Data, and Cloud Computing hold the key to solving this problem. With these technologies, the authors can analyze the real-time and past data of agriculture and make the best decision for problems like crop selection, demand prediction, weather prediction, and many more. Machine Learning algorithms use data and by doing complex computation, try to give an accurate result. In this paper, authors review different research done in the field of food security agriculture using ML (Machine Learning) by using past data and avoiding live data, which makes the model more affordable by decreasing the cost of IoT devices needed for live data.