Fusarium head blight (FHB) is a serious disease that affects wheat and barley production. South Dakota alone suffers $20 million in losses annually from FHB. The early detection of FHB disease will improve the efficiency, accuracy, and capability of FHB resistance screening in wheat, durum, and barley breeding. Such diseases can be automatically or semi-automatically diagnosed using supervised machine learning algorithms and image processing techniques. Several methods have been proposed to plant disease detection. Among these methods, Convolutional Neural Networks (CNNs) are an effective method to automatically identify these diseases. Recently, different approaches have been proposed to boost CNNs’ performance. Most of these methods, however, suffer from a lack of a mechanism that allows them to integrate global and local contextual information adaptively to extract hidden patterns inside the input images. In this regard, we propose the multi-scale attention U-Net. Our main goal is to improve U-net by introducing an attention mechanism at the network bottleneck. In the proposed method, to adaptively emphasize the importance of both spatial and channel dimensions we propose to include the dual attention mechanism. To this end, using the global information of each channel we learn the scaling coefficient to improve the object learning process.  In addition, by learning the self-attention map we impose the spatial attention map on the feature space to adaptively emphasize the important regions. We apply the suggested dual attention in a multi-scale fashion to encourage a multi-scale learning process. To train the proposed network, we captured 12,000 images in an SDSU wheat field in Volga, SD. In addition to covering healthy and diseased plants, these images include different stages of the disease. A group of plant disease experts annotated the prepared images to be used for training the deep model. The model was trained to separate the diseased areas from the healthy areas by receiving the input images using the annotated images. For the first time to the best of our knowledge, we determined the stage of disease based on the segmentation results of the model. As a result, we could detect the diseased areas of the plants, as well as determine the disease level of infection. In the test phase, an automatic plant scanning robot was developed and used to evaluate the performance of the proposed model. The experimental results demonstrate that our approach accurately estimates crop contamination.

ACKNOWLEDGEMENT AND DISCLAIMER
Confidential- please do not share the content of the submission publicly before the 2022 National FHB Forum. This material is based upon work supported by the U.S. Department of Agriculture, under Agreement, 59-0206-2-143, 59-0206-2-153, and FY22-SP-002. This is a cooperative project with the U.S. Wheat & Barley Scab Initiative. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture.