A Prediction Model for Stored Grain Pests Based on Cellular Automaton

An effective prediction for stored grain pests contributes significantly to pest control at the right time, and thereby reduces control cost and environmental pollution. In this paper, aiming at the deficiencies of the existing prediction models, a new prediction method for grain pests based on Cellular Automaton (CA) combining with environmental factors was proposed. First, we simulated tribolium castaneums and cryptolestesferrugineus using CA, and made an evolution rule for the two pests, then simulated their growing trend using three-dimensional CA when there was a struggle for existence between them In a certain environment conditions. At last, we analyzed simulation result, comparing to real experimental result, we proved that as a very effective tool, using CA can predict growing trend of stored grain pests precisely.