[2O4-GS-13] AI application: Agriculture, forestry and fisheries

[2O4-GS-13-03] Automatic pest model construction for pest occurrence simulation

〇Yuuto Kai¹, Noriko Horibe¹, Shin-ichi Aoqui¹ (1. Sojo University)

Keywords:Pest outbreak prediction, Constructive modeling, Machine Learning

Harmful pest occurrence is a serious problem in agricultural management. In order to minimize the damage by harmful pests, prompt and appropriate pest control is necessary, and various systems had been developed as a resolution for the problem. However, the development of such a system is known to be a high cost. Therefore, 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.