@article{fraher_watson_nguyen_moore_lewis_kudenov_yencho_gorny_2024, title={A Comparison of Three Automated Root-Knot Nematode Egg Counting Approaches Using Machine Learning, Image Analysis, and a Hybrid Model}, volume={9}, ISSN={["1943-7692"]}, DOI={10.1094/PDIS-01-24-0217-SR}, abstractNote={spp. (root-knot nematodes [RKNs]) are a major threat to a wide range of agricultural crops worldwide. Breeding crops for RKN resistance is an effective management strategy, yet assaying large numbers of breeding lines requires laborious bioassays that are time-consuming and require experienced researchers. In these bioassays, quantifying nematode eggs through manual counting is considered the current standard for quantifying establishing resistance in plant genotypes. Counting RKN eggs is highly laborious, and even experienced researchers are subject to fatigue or misclassification, leading to potential errors in phenotyping. Here, we present three automated egg counting models that rely on machine learning and image analysis to quantify RKN eggs extracted from tobacco and sweet potato plants. The first method relied on convolutional neural networks trained using annotated images to identify eggs (}, journal={PLANT DISEASE}, author={Fraher, Simon P. and Watson, Mark and Nguyen, Hoang and Moore, Savannah and Lewis, Ramsey S. and Kudenov, Michael and Yencho, G. Craig and Gorny, Adrienne M.}, year={2024}, month={Sep} }
 @article{martinez_kudenov_nguyen_jones_williams_2024, title={Evaluating two high-throughput phenotyping platforms at early stages of the post-harvest pipeline of sweetpotatoes}, volume={8}, ISSN={["2772-3755"]}, url={http://dx.doi.org/10.1016/j.atech.2024.100469}, DOI={10.1016/j.atech.2024.100469}, abstractNote={Recent advancements in artificial intelligence and big data analytics introduce new tools that can enhance the packing efficiency of sweetpotatoes (Ipomoea batatas) (SPs). In this study, we focused on the quantification of inventory as early in the packing process as possible to allow for effective storage planning, smarter inventory selection to fulfill orders, and ultimately reduce the need for refrigeration of excess packed SPs. We built and implemented two scanners to quantify phenotype distributions at different stages of the post-harvest pipeline. Testing and validation were conducted through a collaboration with an industry-partner's packing facility in North Carolina, gaining access to their packing methods, warehouse data, and resources. The first scanner imaged all SPs during the conveyance stage, immediately after they are washed but before they are sorted. The second scanner, positioned to view the top bins after harvest, scanned the top layer of bins on harvesting trucks as they entered the storage warehouse for receiving. We compared the output of our first scanner to the output of a commercial optical sorter under a controlled packing simulation, and then compared our two developed scanners against each other in an observational commercial packing operation. We evaluated millions of SPs, assessing length, width, length-to-width ratio (LW ratio), and weight. We computed a pairwise t-test for each phenotype across scanner pairs and evaluated the Cohen's d effect size to interpret our results. We observed no significant differences in the grade distributions across the scanners, except for the "Giant" weight class, which showed variation between the top bin and eliminator table scanners. In summary, both systems demonstrated promising outcomes, suggesting a potential enhancement in packing efficiency through the timely delivery of comprehensive inventory data.}, journal={SMART AGRICULTURAL TECHNOLOGY}, author={Martinez, Enrique E. Pena and Kudenov, Michael and Nguyen, Hoang and Jones, Daniela S. and Williams, Cranos}, year={2024}, month={Aug} }
 @article{mckee_nguyen_kudenov_christian_2022, title={StarNAV with a wide field-of-view optical sensor}, volume={197}, ISSN={["1879-2030"]}, DOI={10.1016/j.actaastro.2022.04.027}, abstractNote={StarNAV is a method for inferring an observer's velocity from measurements of starlight that have been perturbed by stellar aberration. This usually takes the form of measuring changes in inter-star angles as compared to a reference. Usable velocity estimates require either the measurement of (1) a few inter-star angles with high accuracy or (2) a great many inter-star angles with lower accuracy. This work explores the efficacy of a hypothetical sensor following the latter approach through the use of wide field-of-view (FOV) optical cameras. Key algorithmic considerations for processing large star fields are discussed and an exploration of the design space is performed. A feasible point design with proof-of-concept performance is developed and is shown to fit within a 3U CubeSat form factor.}, journal={ACTA ASTRONAUTICA}, author={McKee, Paul and Nguyen, Hoang and Kudenov, Michael W. and Christian, John A.}, year={2022}, month={Aug}, pages={220–234} }