  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.
