Fusarium Head Blight (FHB) or head scab (caused by Fusarium graminearum), is one of the most devastating diseases affecting small grain cereals including wheat; and causes severe reduction in the quality and quantity of grain yield. The traditional screening of large number of breeding lines for FHB resistance is still dependent on low-throughput visual inspections in the FHB inoculated field nursery followed by visual assessment of percent Fusarium Damaged Kernels (FDKs). Further, the visual assessments are a labor-intensive and time-consuming process and is biased due to the subjectivity and may have lower reproducibility. Some earlier studies have evaluated the effectiveness and preciseness of image analysis approaches to predict FDKs in wheat grain samples; however, only a few focused at the final application, taking into consideration the association between cost and benefit, resolution, and accuracy. In this study, for the first time, we worked with QualySense on a feasibility study aimed at using their proprietary QSorter^® Explorer robotic system for the automatic prediction, sorting and final quality inspection assessment of Fusarium damaged wheat kernels. QSorter® Explorer is powered by advanced mechatronics and artificial intelligence and equipped with a 3D camera and a high resolution Near Infrared Spectrometer active in the 900-1700 nm spectral range. More than 7,200 single kernel images and NIR spectra were collected from healthy and Fusarium damaged kernels to develop machine learning models to predict FDKs and sort samples at 30 kernels/sec. Independent validations of the FDKs module was conducted using 200 advanced breeding lines (100 winter wheat and 100 spring wheat genotypes) that were visually rated by three persons separately. The same samples were analyzed by the QSorter^® Explorer by mean of two classification models FDK-Vision and FDK-NIR which are based respectively on the images and NIR spectra, to predict and sort the FDKs. While the visual evaluations of FDKs by the individuals varied significantly, the QSorter® Explorer consistently produced better results in a time efficient manner. Additionally, compared to visual assessments, QSorter® predictions were more accurate. This automation will certainly make screening for breeding lines more accurate and less prone to personal bias while delivering results faster and streamlining the laboratory efficiency.

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