Fusarium head blight is a devastating disease that causes significant economic losses annually on small grains. Efficiency, accuracy, and timely detection of FHB in the resistance screening are critical for wheat and barley breeding programs. In recent years, various image processing techniques have been developed using supervised machine learning algorithms for the early detection of FHB. The state-of-the-art convolutional neural network-based methods, such as U-Net, employ a series of encoding blocks to create a local representation and a series of decoding blocks to capture the semantic relations. However, these methods are not often capable of long-range modeling dependencies inside the input data, and their ability to model multi-scale objects with significant variations in texture and shape is limited. Vision transformers as alternative architectures with innate global self-attention mechanisms for sequence-to-sequence prediction, due to insufficient low-level details, may also limit localization capabilities. To overcome these limitations, a new Context Bridge is proposed to integrate the local representation capability of the U-Net network in the transformer model. In addition, the standard attention mechanism of the original transformer is replaced with Efficient Self-attention, which is less complicated than other state-of-the-art methods. To train the proposed network, 12,000 wheat images from an FHB-inoculated wheat field at the SDSU research farm in Volga, SD, were captured. In addition to healthy and unhealthy plants, these images encompass various stages of the disease. A team of expert pathologists annotated the images for training and evaluating the developed model. As a result, the effectiveness of the transformer-based method for FHB-disease detection, through extensive experiments across typical tasks for plant image segmentation, is demonstrated.

本研究介绍了一种基于转换器模型的新方法，结合U-Net网络的局部表示能力，并将原始转换器的标准注意机制替换为高效自注意力机制，以提高小麦图像中Fusarium头黑穗病的检测效果。利用12,000张小麦图像进行训练，通过对植物图像分割任务的广泛实验验证了该方法在FHB疾病检测上的效果。

应用高效Transformer模型改进小麦育种中的FHB筛选