A Novel Approach for Management Zone Delineation by Classifying Spatial Multivariate Data and Analyzing Maps of Crop Yield

Abstract

Precision farming has been playing a distinguished role over last few years. It encompasses the techniques of Data Mining and Information Technology into agricultural process. The acute task in classic agriculture is fertilization, which makes minerals available for crops. Site specific methods result in imbalanced management within fields which affects the crop yield. Treating the whole field as uniform area is merely heedless as it forces the farmers to use costly resources like fertilizers, pesticides etc., at greater expenses. As the field is heterogeneous, the critical task is to identify which part of the field should be considered and the percentage of fertilizer or pesticide required. In order to increase the yield productivity, concept of Management Zone Delineation (MZD) has to be adopted, which divides the agricultural field into homogeneous subfields, or zones based on the soil parameters. Precision Agriculture focuses on the utilization of Management zones (MZs). In this paper, we have collected huge data of Davanagere agricultural jurisdiction during standard farming operations which reflects the heterogeneity of agricultural field. We base our work on a new Data Mining technique called Kriging, which interpolates soil sample values for the specific region, which in turn helps in converting heterogeneous zones to homogeneous subfields.