Detecting soybean leaf disease from synthetic image using multi-feature fusion faster R-CNN

Abstract

An accurate detection of soybean leaf disease in soybean field is essential for soybean quality and the agricultural economy. Though many works have been done in identifying soybean leaf disease, because of the insufficient dataset and technical difficulties, the tasks about detecting soybean leaf disease in complex scene are little dressed. This paper develops a synthetic soybean leaf disease image dataset to tackle the problem of insufficient dataset at first. Further, detecting soybean leaf disease in complex scene requires the detection model to be able to precisely discriminate various features, such as features of healthy leaves and diseased leaves, features of leaves with different diseases and so on. Thus, this paper designs a multi-feature fusion Faster R-CNN (MF3 R-CNN) to address the above intractable problem. We obtain the optimal mean average precision with 83.34% in real test dataset. Moreover, the experimental results indicate that the MF3 R-CNN trained only by synthetic dataset is effective in detecting soybean leaf disease in complex scene and superior to the state-of-the-art.