Abstract
Early detection and control of diseases, especially downy mildew, are very important for productivity and quality improvement in viticulture. In this study, we propose a simple method to train and infer the location of downy mildew from hyperspectral images using machine learning for the purpose of early detection and labor saving. Specifically, a vineyard is photographed at a fixed point using a spectrographic camera, and the image is divided into smaller partial images, which are then trained and inferred by the Convolutional Neural Network (CNN).In order to label the training data with "locations where downy mildew has occurred," it is necessary to confirm the presence or absence of downy mildew in the field over a long period of time, which requires a great deal of time and effort. In this study, we compare this method with a simple method of "labeling by photographing time". The results showed that the inference of the location of downy mildew by CNN tended to have a low recall rate and a high precision rate. The results also suggest that even a simple method of "labeling by photographing time" may be able to achieve a certain degree of precision. This suggested the possibility of detecting downy mildew even without data on the details of the location of downy mildew.
