@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{fraher_schwarz_heim_gesteira_mollinari_pereira_zeng_brown-guedira_gorny_yencho_2024, title={Discovery of a major QTL for resistance to the guava root-knot nematode (<i>Meloidogyne enterolobii</i>) in 'Tanzania', an African landrace sweetpotato (<i>Ipomoea batatas</i>)}, volume={137}, ISSN={["1432-2242"]}, DOI={10.1007/s00122-024-04739-1}, abstractNote={Sweetpotato, Ipomoea batatas (L.) Lam. (2n = 6x = 90), is among the world's most important food crops and is North Carolina's most important vegetable crop. The recent introduction of Meloidogyne enterolobii poses a significant economic threat to North Carolina's sweetpotato industry and breeding resistance into new varieties has become a high priority for the US sweetpotato industry. Previous studies have shown that 'Tanzania', a released African landrace, is resistant to M. enterolobii. We screened the biparental sweetpotato mapping population, 'Tanzania' x 'Beauregard', for resistance to M. enterolobii by inoculating 246 full-sibs with 10,000 eggs each under greenhouse conditions. 'Tanzania', the female parent, was highly resistant, while 'Beauregard' was highly susceptible. Our bioassays exhibited strong skewing toward resistance for three measures of resistance: reproductive factor, eggs per gram of root tissue, and root gall severity ratings. A 1:1 segregation for resistance suggested a major gene conferred M. enterolobii resistance. Using a random-effect multiple interval mapping model, we identified a single major QTL, herein designated as qIbMe-4.1, on linkage group 4 that explained 70% of variation in resistance to M. enterolobii. This study provides a new understanding of the genetic basis of M. enterolobii resistance in sweetpotato and represents a major step towards the identification of selectable markers for nematode resistance breeding.}, number={10}, journal={THEORETICAL AND APPLIED GENETICS}, author={Fraher, Simon and Schwarz, Tanner and Heim, Chris and Gesteira, Gabriel De Siqueira and Mollinari, Marcelo and Pereira, Guilherme Da Silva and Zeng, Zhao-Bang and Brown-Guedira, Gina and Gorny, Adrienne and Yencho, G. Craig}, year={2024}, month={Oct} }