Clustering Approaches for Management Zone Delineation in Precision Agriculture for Small Farms

Abstract

For enhancing the quality and the productivity of the crop, usage of modern tools and techniques has become inevitable. One of such technique is Precision Agriculture (PA). PA collects and controls agronomic information to furnish actual nutrient needs to parts of fields rather than average needs to complete fields. These parts of fields are management zones which can be used to treat the within field variation. Management zone delineation (MZD) has become an integral part and pillar of Precision agriculture by dividing the field according to soil physical and chemical characteristics. Concept of clustering from data mining domain is suitable to create such management zones within the field. This paper experiments and compares K mean, FCM, PFCM and LBG clustering algorithm for delineating the management zones in precision agriculture. The objective of determination of zone delineation is for the application of fertilization process. Sugarcane (Saccharum officinarum) has been selected as case study for the experimentation of MZD. It considers 14 important nutrients of the crop for the delineation. Real time data set is generated for the experimentation. Result shows that PFCM algorithm works better over k-mean, FCM and LBG algorithm with spatial data set.