{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T10:40:38Z","timestamp":1783766438364,"version":"3.55.0"},"reference-count":40,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T00:00:00Z","timestamp":1679961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Virginia Small Grains Board"},{"name":"Center for Advanced Innovation in Agriculture at Virginia Tech"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fusarium head blight (FHB) is a disease of small grains caused by the fungus Fusarium graminearum. In this study, we explored the use of hyperspectral imaging (HSI) to evaluate the damage caused by FHB in wheat kernels. We evaluated the use of HSI for disease classification and correlated the damage with the mycotoxin deoxynivalenol (DON) content. Computational analyses were carried out to determine which machine learning methods had the best accuracy to classify different levels of damage in wheat kernel samples. The classes of samples were based on the DON content obtained from Gas Chromatography\u2013Mass Spectrometry (GC-MS). We found that G-Boost, an ensemble method, showed the best performance with 97% accuracy in classifying wheat kernels into different severity levels. Mask R-CNN, an instance segmentation method, was used to segment the wheat kernels from HSI data. The regions of interest (ROIs) obtained from Mask R-CNN achieved a high mAP of 0.97. The results from Mask R-CNN, when combined with the classification method, were able to correlate HSI data with the DON concentration in small grains with an R2 of 0.75. Our results show the potential of HSI to quantify DON in wheat kernels in commercial settings such as elevators or mills.<\/jats:p>","DOI":"10.3390\/s23073523","type":"journal-article","created":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T01:41:34Z","timestamp":1679967694000},"page":"3523","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2105-3297","authenticated-orcid":false,"given":"Kshitiz","family":"Dhakal","sequence":"first","affiliation":[{"name":"School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Upasana","family":"Sivaramakrishnan","sequence":"additional","affiliation":[{"name":"Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuemei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kassaye","family":"Belay","sequence":"additional","affiliation":[{"name":"School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USA"},{"name":"Graduate Program in Genetics, Bioinformatics and Computational Biology, Virginia Tech, Blacksburg, VA 24061, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph","family":"Oakes","sequence":"additional","affiliation":[{"name":"Virginia Tech Eastern Virginia Agricultural Research and Extension Center (AREC), Warsaw, VA 22572, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8668-0163","authenticated-orcid":false,"given":"Xing","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Li","sequence":"additional","affiliation":[{"name":"School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,28]]},"reference":[{"key":"ref_1","unstructured":"Production, F., and Statistics, T. 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