Integrating the degree-days model and google maps to monitor Dubas bug activity in some agricultural regions in Iraq

Abstract EN

In this research, we show the ability to monitor the activity of Dubas bug pest on date palms by proposing an active web forewarning system integrated with web based geospatial application (Google maps API) to enable the agricultural scientists and farmers to show the pest development in their regions by viewing online pest risk graphs and maps.

inverse distance weighting (IDW) method was used to interpolate temperatures data from weather stations to the user identified location.

Single sine model is applied to estimate degree-days due to its practicality in providing a better predictive capability.

two proposed algorithms are designed to simulate the pest growth.

the first algorithm works to produce accurate graphs to show the accumulated degree-days of each pest growth stages in the user specified location and date.

the second algorithm simulate the pest growth during the user specified date as color plotting on the map with a radius of (500) meter.

the proposed system can practically expand its functionality for any pest of interest given its phenological data.

the system tested on 2013 weather data and gives good experimental results.

now it is ready for practical evaluation by specialists to determine its efficiency and applicability.