BLITE-SVR: New forecasting model for late blight on potato using support-vector regression

Various simple statistical methods have been used for the prediction of plant-disease epidemics. However, the need to develop a new model, reflecting many changed environmental factors and applicable to the Korean domestic farmhouse, has been raised. Given this point, we developed the potato late blight prediction model called BLITE-SVR, after which we predicted and verified the first date of occurrence with the data from 1976 to 1985 and from 2009 to 2012 through support-vector regression (SVR), a statistical method offering good performance. For the prediction model, we collected 13 kinds of weather data, including temperature, humidity, evaporation, and so on, which displayed very high correlation to the first date of the occurrence of late blight. The performance of BLITE-SVR has been evaluated through comparison with the conventional moving-average method that was previously used, as well as through pace regression and linear regression. The accuracy of prediction for the first date of occurrence was 64.3% by BLITE-SVR, thus showing a higher degree of accuracy compared with 42.9% by the conventional moving-average method, 42.9% by pace regression and 35.7% by linear regression. This study will enable farmers to match the targeted fungicide application to the time of greatest need and thereby achieve a reduction in chemical use.