Applied Statistical Model and Remote Sensing for Decision Management System for Soybean

This paper proposes a Decision Management System to identify the white mold regions from the soybean fields using Autologistic Statistical Model (ASM) and Remote Sensing (RS) data analysis with commercially available Big Data sets as input data. In order to develop an identification model, numerous types of data need to be considered. In this study, the data that was used is satellite image pixel values, and data gathered from the field such as precipitation, yield, elevation, humidity, wind speed, wind direction and geospatial locations. The model evaluated the outcome using this information as input parameters and provided an overall estimation of the white mold region in the soybean fields. Based on the evaluation of the result, the accuracy rate of the proposed methods 84% which is a promising result due to the fact that each pixel of the satellite image is 30 by 30 meters.