Stem rust, caused by the biotrophic fungal pathogen Puccinia graminis f. sp. tritici ( Pgt), is an important disease of wheat. However, the majority of Pgt virulence/avirulence loci and underlying genes remain uncharacterized due to the constraints of developing bi-parental populations with this obligate biotroph. Genome-wide association studies (GWAS) using a sexual Pgt population mainly collected from the Pacific Northwestern United States were used to identify candidate virulence/avirulence effector genes corresponding to the six wheat Sr genes: Sr5, Sr21, Sr8a, Sr17, Sr9a, and Sr9d. The Pgt isolates were genotyped using whole-genome shotgun sequencing that identified approximately 1.2 million single nucleotide polymorphisms (SNPs) and were phenotyped at the seedling stage on six Sr gene differential lines. Association mapping analyses identified 17 Pgt loci associated with virulence or avirulence phenotypes on six Pgt resistance genes. Among these loci, 16 interacted with a specific Sr gene, indicating Sr-gene specific interactions. However, one avirulence locus interacted with two separate Sr genes ( Sr9a and Sr17), suggesting two distinct Sr genes identifying a single avirulence effector. A total of 24 unique effector gene candidates were identified, and haplotype analysis suggests that within this population, AvrSr5, AvrSr21, AvrSr8a, AvrSr17, and AvrSr9a are dominant avirulence genes, while avrSr9d is a dominant virulence gene. The putative effector genes will be fundamental for future effector gene cloning efforts, allowing for further understanding of rust effector biology and the mechanisms underlying virulence evolution in Pgt with respect to race-specific R-genes. [ ] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Vegetative compatibility groups (VCGs) in fungi like Verticillium dahliae are important for understanding genetic diversity and for informed plant disease management. This study utilized hyperspectral imagery (HSI) and machine learning to differentiate the VCGs of V. dahliae. A total of 194 isolates from VCGs 2B and 4A and 4B were cultured and imaged across the 533–1719 nm spectral range, and the spectral, textural, and morphological features were extracted. The study documented the spectral profiles of V. dahliae’s isolates and identified specific spectral features that can effectively differentiate among the VCGs. Multiple machine learning algorithms, including random forest and artificial neural networks (ANNs), were trained and evaluated on previously unseen isolates. The results showed that combining spectral, textural, and morphological data provided the highest classification accuracy. The ANN model achieved a 79.4% accuracy overall, with an 87% accuracy for VCG 2B and 88% for VCG 4A, but it had consistently low accuracies for VCG 4B. Although this work utilized only three of the nearly eight known VCGs, the findings underscore the potential of the HSI for fungal group classification. The study also highlights the need for future work to include a wider range of VCGs from multiple regions, larger sample sizes, and careful selection of feature sets to enhance model performance and generalizability.

Early and accurate identification and quantification of plant-parasitic nematodes (PPNs) is crucial for their effective control. Although valuable, the current techniques for identifying PPNs, such as morphology and molecular marker-based methods, can be time- and resource-intensive. This study aimed to develop and validate cutting-edge computer vision tools for automated, accurate, and reproducible PPN detection. To achieve this goal, we captured microscopic images of the three economically important PPN genera associated with potato crop: root-lesion nematode (RLN; Pratylenchus spp.), root-knot nematode (RKN; Meloidogyne spp.), and stubby root nematode (SRN; Paratrichodorus and Trichodorus spp.), as well as additional PPNs (PPN-OTHERS) and non-parasitic (NON-PARASITIC) nematodes, for a total of five groups. The captured images (total instances = 8,654) were preprocessed, annotated, and randomly split into three datasets: 75% for training, 15% for validation, and 10% for testing. An object segmentation algorithm, YOLOv11-seg, which predicts each pixel in an image, was trained and evaluated on previously unseen images. The model achieved high accuracy in validation (92.4%) and test (88.6%) datasets, with strong performance for key PPN genera (RKN, RLN, and SRN; F1 scores > 0.92; AUC > 0.93 in the test set). Whereas the NON-PARASITIC group showed strong performance (F1 score > 0.846 and AUC > 0.91), the PPN-OTHERS group performed poorly (test accuracy: 43.9%), frequently misclassified as RLNs and NON-PARASITIC nematodes. The results highlight the potential of artificial intelligence-based tools for identifying PPNs, paving the way for the long-term goal of developing automated detection and quantification systems for plant pathogens.