Pine Wilt Disease (PWD) is a disease causing mass deaths of pine trees in South Korea, and the dead trees serve as breeding grounds for insect vectors responsible for spreading the disease to other host trees. Because the PWD requires early monitoring to minimize its damage on domestic forestry, this study aims to develop a species distribution model for predicting the potential distribution of PWD by using artificial neural network (ANN) with time-series data. Among the architectures, the Convolutional Neural Network exhibited the highest performance, achieving a validation accuracy of 0.854 and a cross-entropy loss of 0.401, and the InceptionTime model emerged as the second-best performer. This study identified the best-performing ANN architecture for a spatiotemporal evaluation of PWD occurrence, emphasizing the importance for determining hyperparameters with ecological characteristics and data types to apply deep learning into SDMs.

• Jae-Min Jung(Department of Smart Agriculture Systems, Chungnam National University, Daejeon, 34134, Korea) Corresponding author
• Sunhee Yoon(Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon, 34134, Korea)
• Jinhyeong Hwang(Division of Monitoring & Analysis, Forest Pests & Diseases Monitoring Headquarters, Korea Forestry Promotion Institute, Daejeon, 35209, Korea)
• Yuri Park(Division of Monitoring & Analysis, Forest Pests & Diseases Monitoring Headquarters, Korea Forestry Promotion Institute, Daejeon, 35209, Korea)
• Wang-Hee Lee(Department of Smart Agriculture Systems, Chungnam National University, Daejeon, 34134, Korea)