Brown Planthopper Sensor Network Optimization Based on Climate and Geographical Factors using Cellular Automata Technique

Abstract

Brown Planthopper (BPH) is one of the most dangerous insects that cause damage to rice. Aphids infected rice fields with low productivity can be lost even. Dealing with this situation, the Plant Protection industry has invented the light trap - a device based on the specific activity of insects phototaxis. These measures are considered effective and less costly today. However, the current light traps are usually installed next to the home of the staff assigned to manage light traps for easy tracking without attention to the impact of environmental factors around. Currently, the plant protection industry wants more scientific basis in light traps arranged so they want to review and make the factors of climate and geography in the light traps installed but not yet performed. In this paper, we propose an approach to find appropriate positions to replace light traps based on a combination between weather factors and geographical factors with data on infected areas by BPH with various infection levels exhibited on the maps based on Cellular Automata method. We present the simulation results with 8 considered cases to determine positions for light traps in an area of more than 1400 square kilometres including 84 communes in Can Tho city, one of the largest rice granaries in Vietnam.