A Median Strange Point algorithm for Delineation of Agricultural Management Zones - P.Janrao, P; Mishra, D. S; Bharadi, V. A (International Journal of Computer Sciences and Engineering, 2020)
Use of Precision Agriculture (PA) is the need of an hour to enhance the crop productivity to meet the increasing demand of food supply. Clustering algorithms have been proven to be the best suitable ones to delineate the management zones (as per soil fertility) in PA. Management zones can be treated as sub-fields, which are homogeneous in soil physical/chemical properties. In this paper we have proposed a median strange point (MSP) clustering algorithm for the delineation of agricultural management zones. The median strange point algorithm has been compared with the popular clustering algorithms like K-means, Fuzzy C Mean, Possiblistic Fuzzy C Means and Linde Buzo Gray algorithms. The results obtained demonstrated that for the given number of management zones the median strange point algorithm outputs are at par; in some cases superior than the standard algorithms. The proposed experimentation is carried out on the Sugarcane (Saccharum Officinarum) datastet of a small farm of size 2.83ha (7 acres) in Kanhegaon village, Ahmednagar (Maharashtra), India.