@article{winn_acharya_ward_lyerly_griffey_fitzgerald_dong_cowger_murphy_brown‐guedira_2025, title={Genetic mapping of resistance to <i>Fusarium</i> head blight in soft red winter wheat line NC13‐20076}, DOI={10.1002/csc2.70022}, abstractNote={Abstract Fusarium head blight (FHB) infection causes yield loss, quality degradation, and the production of damaging mycotoxins in common wheat ( Triticum aestivum L). Marker analysis suggests that NC13‐20076 does not possess previously identified FHB resistance quantitative trait loci (QTL) screened for in eastern winter wheat germplasm. A doubled haploid population of 168 lines from the cross of GA06493‐13LE6 and NC13‐20076 was phenotyped in inoculated nurseries in six environments. Heading date, plant height, and visual ratings of Fusarium damage on heads were recorded in the field; percent Fusarium damaged kernels (FDK) and deoxynivalenol (DON) accumulation were recorded post‐harvest. Interval and multiple QTL mapping were performed on each environment‐by‐trait combination. Plant height and heading date QTL were identified on chromosomes 4A, 5A, 6A, and 7B, and peak markers were used as covariates in mapping of disease response traits. Disease response QTL were identified on chromosomes 1A, 2A, 2B, 3A, 3B, 4A, 5A, 7A, and 7D. The largest percent variance (PV) QTL identified for FHB visual ratings (10.8%) and DON accumulation (10.1%) were found on chromosome 5A ( QFvr.nc‐5A , QDon.nc‐5A ). The largest PV (10.3%) QTL identified for FDK were found on 1A ( QFdk.nc‐1A ). Disease response QTL for multi‐environment scans of visual ratings, FDK, and DON accumulation accounted for 4.0%–10.8%, 4.1%–10.3%, and 4.9%–10.1% of the total variance, respectively. The present results indicate that NC13‐20076 contains several FHB response QTL, which overlap with previously identified QTL and demonstrate the importance of NC13‐20076 as a readily accessible source of FHB resistance.}, journal={Crop Science}, author={Winn, Z. J. and Acharya, R. and Ward, B. and Lyerly, J. and Griffey, C. and Fitzgerald, J. and Dong, Y. and Cowger, C. and Murphy, J. P. and Brown‐Guedira, G.}, year={2025}, month={Mar} }
 @article{navasca_bazrafkan_dariva_kim_worral_johnson_acharya_piche_ross_raymon_et al._2025, title={Improving estimation of days to maturity in field pea using RGB aerial imagery and machine learning}, volume={8}, DOI={10.1002/ppj2.70038}, abstractNote={Abstract Accurately estimating days to maturity (DTM) is essential for assessing local adaptation and yield potential in field pea ( Pisum sativum L.) breeding programs. However, traditional manual scoring of DTM is labor‐intensive and inefficient for large‐scale, multi‐environment trials. To address this challenge, we developed a high‐throughput, low‐cost phenotyping framework using uncrewed aerial systems (UASs) equipped with red‐green‐blue cameras, implemented within the North Dakota State University Pulse Crop Breeding Program. This study aimed to (1) compare aerial and manual phenotyping for DTM estimation, (2) identify the optimal assessment time point, and (3) detect significant loci associated with DTM in a panel of 300 genetically diverse pea accessions. Image‐derived vegetation indices (VIs) collected 71 days after planting exhibited strong correlations with manually assessed DTM. Notably, vegetation indices demonstrated higher heritability ( H 2 = 0.91) compared to traditional DTM scores ( H 2 = 0.84). eXtreme Gradient Boosting models identified the visible atmospherically resistant index (31%), modified green‐red vegetation index (17%), and redness index (13%) as the most predictive VIs. Genome‐wide association mapping using these indices revealed three significant single nucleotide polymorphisms on chromosomes 3 and 5—variants not detected using traditional maturity data—highlighting the potential enhanced detection power of image‐derived traits. This work demonstrates the utility of low‐cost UAS platforms for scalable, non‐destructive maturity estimation and illustrates their potential to uncover genetic components of economically important traits, offering new avenues for addressing missing heritability in legume breeding.}, number={1}, journal={The Plant Phenome Journal}, author={Navasca, Harry and Bazrafkan, Aliasghar and Dariva, Françoise Dalprá and Kim, Jeong‐Hwa and Worral, Hannah and Johnson, Josephine Princy and Acharya, Shailesh Raj and Piche, Lisa and Ross, Andrew and Raymon, Garrett and et al.}, year={2025}, month={Sep} }