The Methodology for Generationg Agricultural Weather Information Using Deep Learning

It is well known that the most important factor affecting agriculture is weather. The demand for agricultural weather information for agriculture is high, but research on this is still lacking. This study deals with methods for producing weather informations for onion cultivation in South Korea. A deep learning method was applied to generate agricultural weather information. A deep learning is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. There is a difficulty in producing weather information of the field because there is no observed value of onion field. Interpolating methods based on statistical spatio-temporal model were applied to Glosea 5 predictive value. Inverse distance weight method, generalized additive model, Bayesian spatial model and Bayesian spatial temporal were considered for interpolation. However, there is a limit of spatial uncertainty because of interpolation. More research is needed to reduce uncertainty.