WATER DEMAND PREDICTION IN IRRIGATION SYSTEM USING KNN ALGORITHM

Abstract

Increasing food demand will challenge the agricultural sector globally over the next decades. A sustainable solution to this challenge is to increase crop yield without massive cropland area expansion. This can be achieved by identifying and adopting best management practices. The more detailed understanding of how crop yield is impacted by climate change and growing-season weather. Many factors influence irrigation water requirement in an agriculture field. Those factors are age of plant, humidity, temperature, soil moisture/soil water needed. Despite the multiple solution proposed, still the quantity of water over flood and underfloor in the agriculture felid. The artificial influence on irrigation requirement should be thought of an important impact factor, considering the requirement of water, the technology can help in preserving large quantity of water in agriculture felid. This system will predict the balance between water supply and demand requires efficient water supply system by using K-nearest neighbor (KNN). In this system, the C# programming language is implemented on Microsoft Visual Studio ID and Microsoft SQL Server is also used for Database Engine.