Fusarium head blight (FHB), caused primarily by Fusarium graminearum Schwabe, is one of the most common and devastating fungal diseases, causing significant losses in wheat grain productivity and quality by producing harmful mycotoxins. Traditionally, wheat breeders mainly focus on visual selection for FHB resistant lines during FHB resistance breeding in wheat. Advanced techniques that enable high-precision and quick identification are required to improve its detection, because the current method i.e., visual assessment is labour- and time-intensive and subject to subjectivity biases. As a result, methods with a higher degree of objectivity have been investigated, and special emphasis has been laid on the use of high-resolution imaging (e.g., RGB and thermal) coupled with advanced deep learning methods as the foundation for more reliable detection strategies. In this study, first, infected spikes from the wheat genotypes grown in FHB nursery were visually rated and grouped into four different classes including resistant (0-25%), moderately resistant (26-50%), moderately susceptible (51-70%), and susceptible (71-100%) based on the level of FHB infections. Following this, high-resolution RGB images of FHB infected wheat spikes were collected from the same visually rated FHB inoculated genotypes. Commonly used Convolutional Neural Network (CNN) architectures such as VGG and ResNet etc.) were employed to develop FHB infection level detection models. Additionally, to achieve enhanced model performance in the context of relatively small sample size circumstances, deep transfer learning strategy was adopted as well. To examine and compare the accuracies of different modelling strategies and CNN architectures, independent testing data was used for model assessment. The confusion/error matrix, along with the overall accuracy, Kappa coefficient and F1 score matrix were used for model evaluation. This study shows that high-resolution RGB imaging combined advanced deep learning approach can be used to predict FHB infection in seeds within the spikelet’s and to identify very small differences in spike architecture (associated with FHB infection), which can better help breeders predict FHB infection at field level.

ACKNOWLEDGEMENT AND DISCLAIMER
This material is based upon work supported by the U.S. Department of Agriculture, under Agreement 59-0206-2-153. 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