Universal Simulation System by Learning from Historical Data of Agricultural Pest Occurrence

In agricultural management, harmful pest occurrences are very serious problems for achieving farmer's stable income. Speedy and appropriate pest control are necessary to minimize harmful pest damages. However, it is difficult to realize such pest controls because many experts or systems with high costs are needed essentially in considerable traditional methods. In this research, we suppose a universal simulation system as one of the solutions for the problem. The system can be applied to various kind of it is important to develop a technology to realize systems in rapid and low cost. In this research, we propose a method to generate pest models, which is one of the most important components for pest occurrence simulation systems. Weather information and past pest occurrence data are used by machine learning algorithm “C 4. 5” to find hypotheses which represent the relationship between them. Each pest model is automatically generated based on the hypotheses, and the model is refined by comparing their behavior with real cultivation experiments.