Clustering Approaches for Management Zone Delineation in Precision Agriculture for Small Sugarcane Farm in India

Abstract

It is expected that the world population will cross the number 9.9 billion by 2050.To cater newlinethe increasing demand of the food for the ever growing population; the use of modern tools and newlinetechniques will become inevitable for increasing the yield of the crops. A way to solve this newlineconundrum is through understanding the site specific requirement of nutrients using Precision newlineAgriculture techniques (PA). To increase the farm produce and the economic returns, Precision newlineAgriculture (PA) provides the modern techniques with reduced environmental impact. newlinePA is a technique which uses information technology for enhancing the productivity and newlinethe quality of the crops. The studies show that there is variation in the distribution of the newlinenutrients within the field. PA essentially collects and controls agronomic information to furnish newlineactual nutrient needs to parts of fields rather than average needs to the entire fields. These parts newlineof fields are called as management zones which can be used to treat the within field variation. newlineA management zone can be defined as a part of a field that possesses homogeneous soil newlinephysical and chemical characteristics. The delineation of management zones is a simple way of newlineclassifying the spatial variability within a field. The Variable Rate Technique (VRT) can be newlineused to treat the within field variation with the help of the defined management zones. newlineManagement zone delineation (MZD) has become an integral part and pillar of precision newlineagriculture. Studies have shown that the implementation of PA is promising with respect to newlineincreased yield and lesser environmental impact. newlineMZD in PA can be achieved using the data mining techniques. Clustering is the most newlinesuitable technique for delineating the management zones in PA. Clustering groups the similar newlinedata objects together in a group called clusters, while the dissimilar data objects are grouped newlineseparately in different clusters.