Machine Learning Approaches for Smart Agriculture

Abstract

India's agricultural sector is an essential component of the country's economy. More over half of the population in India relies on agriculture. This is high time that the Country needs to focus on improvisation in the smart agriculture. Machine learning is the wide perspective technique which is massively occupying its role in many of the worldwide areas. Among the different range of Machine Learning applications, smart agriculture has been fascinating every researcher’s work area. By reviewing the crop statistics system in India which pictures the challenges in the qualitative field monitoring system. All these can be enhanced by replacing the smart agriculture system using ML. The learning of the agricultural field includes plant management, crop and yield management, soil management, disease management, weed management, water management, animal tracking etc., Full Farm management can be improved further by using Machine Learning to sensor data Artificial Intelligence system Application which provides more suggestions in decision making. Plant management system can be improved by adopting ANN based Machine learning Algorithm. For crop, soil and weed management, ML open ups many opportunities by improving the data collection. A defined data collection of the agro-farm is flexible enough to work efficiently in ML platform. In this article, different approaches using ML algorithm to make predictions for smart agriculture can be reviewed.