Machine Learning Applications in Agriculture

Abstract

Agriculture plays a vital role in the economic growth of the country. To meet out the food requirement of the increase of population is a challenging task with frequent changes in climatic conditions and limited resources. Smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine has the ability to learn without being explicitly programmed. Machine Learning together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. Machine Learning applications in the field of agriculture are explained in this article. The areas that are focused are prediction of soil parameters such as moisture content, crop yield prediction, disease and weed detection in crops, Identify water stress in plant, Crop mapping , Crop selection prediction , Ground water level prediction: Groundwater is the largest storage of freshwater resources, which serves as the and species detection. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also discussed to reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.