Scab is one of the most important diseases in wheat. Rapid and accurate detection of wheat scab under farmland conditions is essential for timely and effectively managing the disease. This study proposes a method for automatically detecting wheat scab by using remote sensing from unmanned aerial vehicles (UAVs). In the method, contrast enhancement was carried out on acquired RGB images of wheat to highlight the diseased spots, and then an adaptively spatial feature fusion network (ASFFNet) was constructed to detect wheat scab in the images. ASFFNet used the feature enhancement module to combine the global and local features of RGB images of wheat to improve the expression ability of these features. In addition, the feature fusion module in ASFFNet adaptively fused the enhanced features at multiple scales to solve the inconsistency of features at different scales during fusion caused by too small disease areas, which improved the detection precision. The results show that the proposed method has a higher AP (average precision) than the existing object detection algorithms, single shot MultiBox detector (SSD), RetinaNet, YOLOv3 (you only look once version 3) and YOLOv4 (you only look once version 4). The proposed method can be a practical way to handle the scab detection task using UAV images. It also can provide technical references for farmland-level wheat phenotype monitoring.

黑星病是小麦最主要的病害之一。在农田条件下快速准确地检测小麦黑星病对于及时有效地控制疾病至关重要。本研究提出了一种利用无人机遥感自动检测小麦黑星病的方法。该方法对获取的小麦RGB图像进行对比度增强以突出病斑，然后构建自适应空间特征融合网络（ASFFNet）检测图像中的小麦黑星病。ASFFNet 使用特征增强模块将小麦 RGB 图像的全局和局部特征结合起来，以提高这些特征的表达能力。此外，ASFFNet中的特征融合模块自适应融合了多尺度的增强特征，解决了融合时因病变区域过小导致的不同尺度特征不一致的问题，提高了检测精度。结果表明，与现有的目标检测算法、单次多框检测器 (SSD)、RetinaNet、YOLOv3 (you only look once version 3) 和 YOLOv4 (you only look once version 4) 相比，所提出的方法具有更高的 AP（平均精度） ). 所提出的方法可以成为使用无人机图像处理结痂检测任务的实用方法。可为农田级小麦表型监测提供技术参考。结果表明，与现有的目标检测算法、单次多框检测器 (SSD)、RetinaNet、YOLOv3 (you only look once version 3) 和 YOLOv4 (you only look once version 4) 相比，所提出的方法具有更高的 AP（平均精度） ). 所提出的方法可以成为使用无人机图像处理结痂检测任务的实用方法。可为农田级小麦表型监测提供技术参考。结果表明，与现有的目标检测算法、单次多框检测器 (SSD)、RetinaNet、YOLOv3 (you only look once version 3) 和 YOLOv4 (you only look once version 4) 相比，所提出的方法具有更高的 AP（平均精度） ). 所提出的方法可以成为使用无人机图像处理结痂检测任务的实用方法。可为农田级小麦表型监测提供技术参考。