Infrastructure for data-driven agriculture: identifying management zones for cotton using statistical modeling and machine learning techniques

Advances in many areas of sensing technologies and the widespread use and greater accuracy of global positioning systems offer the prospect of improving agricultural productivity through the intensive use of data. By nature, agriculture is a spatial science characterized by significant variability in terms of yield and concentration of pests and plant diseases. Consequently, precision agriculture seeks to improve the effectiveness of various types of sensing information to give the grower more data and the ability to design the specific treatments for site-specific management of inputs and outputs. The intensive use of data in agriculture is at a relatively early stage and there remains much opportunity to refine modeling approaches and to build information infrastructure. With the overall goal of optimizing inputs to achieve the maximum output in terms of yield, this paper focuses on the application of a clustering algorithm to field data with the goal to identify management zones. We employ two sets of attributes, first yield and second field properties like slope and electrical conductivity to delineate the management zones. By definition, a management zone is a contiguous area defined by one or more features and may take on many different shapes. Building on the established machine learning approach of k-means clustering, we successfully identify a near optimal number of management zones for a cotton field.