A deep learning approach for on-site plant leaf detection

Plant diseases are the major problem in the worldwide agriculture sector. Therefore, the early detection is essential for reducing economic losses and mitigating the seriousness of the global food problem. Some fast and accurate computer-based methods have been applied to detect plant diseases. However, as far as our best knowledge, all those methodologies only accept a narrow range image, typically one or limited number of target(s) are in the image frame as their input. Thus, they are time-consuming and difficult to be applied for on-site wide range images (e.g. images or videos from stationary surveillance camera). In this paper, we propose leaf localization method from on-site wide-angle images with a deep learning approach. Our method achieves a detection performance of 78.0% in F1-measure at 2.0 fps.