  Many existing techniques provide automatic estimation of crop damage due to
various diseases. However, early detection can prevent or reduce the extend of
damage itself. The limited performance of existing techniques in early
detection is lack of localized information. We instead propose a dataset with
annotations for each diseased segment in each image. Unlike existing
approaches, instead of classifying images into either healthy or diseased, we
propose to provide localized classification for each segment of an images. Our
method is based on Mask RCNN and provides location as well as extend of
infected regions on the plant. Thus the extend of damage on the crop can be
estimated. Our method has obtained overall 87.6% accuracy on the proposed
dataset as compared to 58.4% obtained without incorporating localized
information.
