In agricultural ecosystems, the relationship between insect pests and hosts is important, as insect pests can invade hosts, increasing insect pest density that threatens the hosts’ health. Insect pests and hosts are negatively correlated and affect the environment around them. i.e., host health, environment, and insect pest density are causally related, and the environment affects insect pest density. Deep learning is method of machine learning based on neural network theory. This approach enables handling uncertain environmental factors that simultaneously impact the density of F. occidentalis. Environmental factors affecting the density fluctuation of F. occidentalis selected atmosphere factors, soil factors, and host factors. This study aims to F. occidentalis monitoring using deep learning models inputting environmental factors.

• Taechul Park(Department of Plant Medicine, Gyeongsang National University, Jinju 52828, Korea)
• SoEun Eom(Department of Plant Medicine, Gyeongsang National University, Jinju 52828, Korea)
• Ji-won Jeong(Department of Plant Medicine, Gyeongsang National University, Jinju 52828, Korea)
• Jung-Joon Park(Department of Plant Medicine, Gyeongsang National University, Jinju 52828, Korea, Institute of Agriculture and Life Science, Gyeongsang National University, Jinju 52828, Korea)