A Weakly Supervised Approach for Disease Segmentation of Maize Northern Leaf Blight from UAV Images

Abstract

The segmentation of crop disease zones is an important task of image processing since the knowledge of the growth status of crops is critical for agricultural management. Nowadays, images taken by unmanned aerial vehicles (UAVs) have been widely used in the segmentation of crop diseases, and almost all current studies use the study paradigm of full supervision, which needs a large amount of manually labelled data. In this study, a weakly supervised method for disease segmentation of UAV images is proposed. In this method, auxiliary branch block (ABB) and feature reuse module (FRM) were developed. The method was tested using UAV images of maize northern leaf blight (NLB) based on image-level labels only, i.e., only the information as to whether NBL occurs is given. The quality (intersection over union (IoU) values) of the pseudo-labels in the validation dataset achieved 43% and the F1 score reached 58%. In addition, the new method took 0.08 s to generate one pseudo-label, which is highly efficient in generating pseudo-labels. When pseudo-labels from the train dataset were used in the training of segmentation models, the IoU values of disease in the test dataset reached 50%. These accuracies outperformed the benchmarks of the ACoL (45.5%), RCA (36.5%), and MDC (34.0%) models. The segmented NLB zones from the proposed method were more complete and the boundaries were more clear. The effectiveness of ABB and FRM was also explored. This study is the first time supervised segmentation of UAV images of maize NLB using only image-level data was applied, and the above test results confirm the effectiveness of the proposed method.