Application of a U-Net Neural Network to the Puccinia sorghi-Maize Pathosystem.

Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can present a challenge for pipeline development. Here, we describe a machine learning approach to quantify Puccinia sorghi incidence on maize leaves utilizing U-Net convolutional neural network models. We analyzed several U-Net models with increasing amounts of training image data, either randomly chosen from a large data pool or randomly chosen from a subset of disease time course data. As the training dataset size increases, the models perform better, but the rate of performance decreases. Additionally, the use of a diverse training dataset can improve model performance and reduce the amount of annotated training data required for satisfactory performance. Models with as few as 48 whole-leaf training images are able to replicate the ground truth results within our testing dataset. The final model utilizing our entire training dataset performs similarly to our ground truth data, with an intersection over union value of 0.5002 and an F1 score of 0.6669. This work illustrates the capacity of U-Nets to accurately answer real-world plant pathology questions related to quantification and estimation of plant disease symptoms. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize.

Breeding for disease resistance is a central component of strategies implemented to mitigate biotic stress impacts on crop yield. Conventionally, genotypes of a plant population are evaluated through a labor-intensive process of assigning visual scores (VS) of susceptibility (or resistance) by specifically trained staff, which limits manageable volumes and repeatability of evaluation trials. Remote sensing (RS) tools have the potential to streamline phenotyping processes and to deliver more standardized results at higher through-put. Here, we use a two-year evaluation trial of three newly developed biparental populations of maize doubled haploid lines (DH) to compare the results of genomic analyses of resistance to common rust (CR) when phenotyping is either based on conventional VS or on RS-derived (vegetation) indices. As a general observation, for each population × year combination, the broad sense heritability of VS was greater than or very close to the maximum heritability across all RS indices. Moreover, results of linkage mapping as well as of genomic prediction (GP), suggest that VS data was of a higher quality, indicated by higher -logp values in the linkage studies and higher predictive abilities for genomic prediction. Nevertheless, despite the qualitative differences between the phenotyping methods, each successfully identified the same genomic region on chromosome 10 as being associated with disease resistance. This region is likely related to the known CR resistance locus Rp1. Our results indicate that RS technology can be used to streamline genetic evaluation processes for foliar disease resistance in maize. In particular, RS can potentially reduce costs of phenotypic evaluations and increase trialing capacities.

Resistance in Maize (Zea mays) to Isolates of Puccinia sorghi from Eastern Australia.

The causal agent of maize common rust (CR), Puccinia sorghi, has increased in incidence and severity in Australia in recent years, prompting the assessment of sources of resistance and a preliminary survey of the diversity of P. sorghi populations. The maize commercial hybrids tested carried no resistance to 14 isolates of P. sorghi and had infection types comparable with that of a susceptible check. The resistance gene Rp1_D that remained effective in the United States for 35 years was ineffective against 7 of the 14 isolates. Maize lines carrying known "resistance to Puccinia" (Rp) genes were inoculated with the five isolates considered most diverse based on year of collection (2018 or 2019), location (Queensland or Victoria), and host from which they were isolated (maize or sweet corn). Lines carrying the resistance genes RpG, Rp5, Rp1_E, Rp1_I, Rp1_L, RpGDJ, RpGJF, and Rp5GCJ were resistant to all five isolates and to isolates collected in many agroecological regions. These lines were recommended as donors of effective resistance for maize breeding programs in Australia. Lines carrying no known resistance or resistance genes Rp8_A, Rp8_B, Rp1_J, Rp1_M, Rp7, and Rpp9 (conferring resistance to P. polysora) were susceptible to all five isolates. Differential lines carrying resistance genes Rp1_B, Rp1_C, Rp1_D, Rp1_F, Rp1_K, Rp3_D, or Rp4_A were either resistant or susceptible depending upon the isolate used, showing that the isolates varied in virulence for these genes. Urediniospore production was reduced on adult compared with juvenile plants, presumably due to changes in plant physiology associated with age or the presence of adult plant resistance.

Combination of Linkage Mapping, GWAS, and GP to Dissect the Genetic Basis of Common Rust Resistance in Tropical Maize Germplasm.

Common rust (CR) caused by Puccina sorghi is one of the destructive fungal foliar diseases of maize and has been reported to cause moderate to high yield losses. Providing CR resistant germplasm has the potential to increase yields. To dissect the genetic architecture of CR resistance in maize, association mapping, in conjunction with linkage mapping, joint linkage association mapping (JLAM), and genomic prediction (GP) was conducted on an association-mapping panel and five F3 biparental populations using genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). Analysis of variance for the biparental populations and the association panel showed significant genotypic and genotype x environment (GXE) interaction variances except for GXE of Pop4. Heritability (h2) estimates were moderate with 0.37-0.45 for the individual F3 populations, 0.45 across five populations and 0.65 for the association panel. Genome-wide association study (GWAS) analyses revealed 14 significant marker-trait associations which individually explained 6-10% of the total phenotypic variances. Individual population-based linkage analysis revealed 26 QTLs associated with CR resistance and together explained 14-40% of the total phenotypic variances. Linkage mapping revealed seven QTLs in pop1, nine QTL in pop2, four QTL in pop3, five QTL in pop4, and one QTL in pop5, distributed on all chromosomes except chromosome 10. JLAM for the 921 F3 families from five populations detected 18 QTLs distributed in all chromosomes except on chromosome 8. These QTLs individually explained 0.3 to 3.1% and together explained 45% of the total phenotypic variance. Among the 18 QTL detected through JLAM, six QTLs, qCR1-78, qCR1-227, qCR3-172, qCR3-186, qCR4-171, and qCR7-137 were also detected in linkage mapping. GP within population revealed low to moderate correlations with a range from 0.19 to 0.51. Prediction correlation was high with r = 0.78 for combined analysis of the five F3 populations. Prediction of biparental populations by using association panel as training set reveals positive correlations ranging from 0.05 to 0.22, which encourages to develop an independent but related population as a training set which can be used to predict diverse but related populations. The findings of this study provide valuable information on understanding the genetic basis of CR resistance and the obtained information can be used for developing functional molecular markers for marker-assisted selection and for implementing GP to improve CR resistance in tropical maize.

Characterization and comparative analysis of the genome of Puccinia sorghi Schwein, the causal agent of maize common rust.

Rust fungi are one of the most devastating pathogens of crop plants. The biotrophic fungus Puccinia sorghi Schwein (Ps) is responsible for maize common rust, an endemic disease of maize (Zea mays L.) in Argentina that causes significant yield losses in corn production. In spite of this, the Ps genomic sequence was not available. We used Illumina sequencing to rapidly produce the 99.6Mbdraft genome sequence of Ps race RO10H11247, derived from a single-uredinial isolate from infected maize leaves collected in the Argentine Corn Belt Region during 2010. High quality reads were obtained from 200bppaired-end and 5000bpmate-paired libraries and assembled in 15,722 scaffolds. A pipeline which combined an ab initio program with homology-based models and homology to in planta enriched ESTs from four cereal pathogenic fungus (the three sequenced wheat rusts and Ustilago maydis) was used to identify 21,087 putative coding sequences, of which 1599 might be part of the Ps RO10H11247 secretome. Among the 458 highly conserved protein families from the euKaryotic Orthologous Groups (KOG) that occur in a wide range of eukaryotic organisms, 97.5% have at least one member with high homology in the Ps assembly (TBlastN, E-value⩽e-10) covering more than 50% of the length of the KOG protein. Comparative studies with the three sequenced wheat rust fungus, and microsynteny analysis involving Puccinia striiformis f. sp. tritici (Pst, wheat stripe rust fungus), support the quality achieved. The results presented here show the effectiveness of the Illumina strategy for sequencing dikaryotic genomes of non-model organisms and provides reliable DNA sequence information for genomic studies, including pathogenic mechanisms of this maize fungus and molecular marker design.

The durable wheat disease resistance gene Lr34 confers common rust and northern corn leaf blight resistance in maize.

Maize (corn) is one of the most widely grown cereal crops globally. Fungal diseases of maize cause significant economic damage by reducing maize yields and by increasing input costs for disease management. The most sustainable control of maize diseases is through the release and planting of maize cultivars with durable disease resistance. The wheat gene Lr34 provides durable and partial field resistance against multiple fungal diseases of wheat, including three wheat rust pathogens and wheat powdery mildew. Because of its unique qualities, Lr34 became a cornerstone in many wheat disease resistance programmes. The Lr34 resistance is encoded by a rare variant of an ATP-binding cassette (ABC) transporter that evolved after wheat domestication. An Lr34-like disease resistance phenotype has not been reported in other cereal species, including maize. Here, we transformed the Lr34 resistance gene into the maize hybrid Hi-II. Lr34-expressing maize plants showed increased resistance against the biotrophic fungal disease common rust and the hemi-biotrophic disease northern corn leaf blight. Furthermore, the Lr34-expressing maize plants developed a late leaf tip necrosis phenotype, without negative impact on plant growth. With this and previous reports, it could be shown that Lr34 is effective against various biotrophic and hemi-biotrophic diseases that collectively parasitize all major cereal crop species.

Dissection of Common Rust Resistance in Tropical Maize Multiparent Population through GWAS and Linkage Studies.

Common rust (CR), caused by Puccina sorghi, is a major foliar disease in maize that leads to quality deterioration and yield losses. To dissect the genetic architecture of CR resistance in maize, this study utilized the susceptible temperate inbred line Ye107 as the male parent crossed with three resistant tropical maize inbred lines (CML312, D39, and Y32) to generate 627 F7 recombinant inbred lines (RILs), with the aim of identifying maize disease-resistant loci and candidate genes for common rust. Phenotypic data showed good segregation between resistance and susceptibility, with varying degrees of resistance observed across different subpopulations. Significant genotype effects and genotype × environment interactions were observed, with heritability ranging from 85.7% to 92.2%. Linkage and genome-wide association analyses across the three environments identified 20 QTLs and 62 significant SNPs. Among these, seven major QTLs explained 66% of the phenotypic variance. Comparison with six SNPs repeatedly identified across different environments revealed overlap between qRUST3-3 and Snp-203,116,453, and Snp-204,202,469. Haplotype analysis indicated two different haplotypes for CR resistance for both the SNPs. Based on LD decay plots, three co-located candidate genes, Zm00001d043536, Zm00001d043566, and Zm00001d043569, were identified within 20 kb upstream and downstream of these two SNPs. Zm00001d043536 regulates hormone regulation, Zm00001d043566 controls stomatal opening and closure, related to trichome, and Zm00001d043569 is associated with plant disease immune responses. Additionally, we performed candidate gene screening for five additional SNPs that were repeatedly detected across different environments, resulting in the identification of five candidate genes. These findings contribute to the development of genetic resources for common rust resistance in maize breeding programs.

Analysis of the transcriptomic, metabolomic, and gene regulatory responses to Puccinia sorghi in maize.

Common rust, caused by Puccinia sorghi, is a widespread and destructive disease of maize. The Rp1-D gene confers resistance to the P. sorghi IN2 isolate, mediating a hypersensitive cell death response (HR). To identify differentially expressed genes (DEGs) and metabolites associated with the compatible (susceptible) interaction and with Rp1-D-mediated resistance in maize, we performed transcriptomics and targeted metabolome analyses of P. sorghi IN2-infected leaves from the near-isogenic lines H95 and H95:Rp1-D, which differed for the presence of Rp1-D. We observed up-regulation of genes involved in the defence response and secondary metabolism, including the phenylpropanoid, flavonoid, and terpenoid pathways. Metabolome analyses confirmed that intermediates from several transcriptionally up-regulated pathways accumulated during the defence response. We identified a common response in H95:Rp1-D and H95 with an additional H95:Rp1-D-specific resistance response observed at early time points at both transcriptional and metabolic levels. To better understand the mechanisms underlying Rp1-D-mediated resistance, we inferred gene regulatory networks occurring in response to P. sorghi infection. A number of transcription factors including WRKY53, BHLH124, NKD1, BZIP84, and MYB100 were identified as potentially important signalling hubs in the resistance-specific response. Overall, this study provides a novel and multifaceted understanding of the maize susceptible and resistance-specific responses to P. sorghi.

Genetic Dissection of Quantitative Resistance to Common Rust (Puccinia sorghi) in Tropical Maize (Zea mays L.) by Combined Genome-Wide Association Study, Linkage Mapping, and Genomic Prediction.

Common rust is one of the major foliar diseases in maize, leading to significant grain yield losses and poor grain quality. To dissect the genetic architecture of common rust resistance, a genome-wide association study (GWAS) panel and a bi-parental doubled haploid (DH) population, DH1, were used to perform GWAS and linkage mapping analyses. The GWAS results revealed six single-nucleotide polymorphisms (SNPs) significantly associated with quantitative resistance of common rust at a very stringent threshold of P-value 3.70 × 10-6 at bins 1.05, 1.10, 3.04, 3.05, 4.08, and 10.04. Linkage mapping identified five quantitative trait loci (QTL) at bins 1.03, 2.06, 4.08, 7.03, and 9.00. The phenotypic variation explained (PVE) value of each QTL ranged from 5.40 to 12.45%, accounting for the total PVE value of 40.67%. Joint GWAS and linkage mapping analyses identified a stable genomic region located at bin 4.08. Five significant SNPs were only identified by GWAS, and four QTL were only detected by linkage mapping. The significantly associated SNP of S10_95231291 detected in the GWAS analysis was first reported. The linkage mapping analysis detected two new QTL on chromosomes 7 and 10. The major QTL on chromosome 7 in the region between 144,567,253 and 149,717,562 bp had the largest PVE value of 12.45%. Four candidate genes of GRMZM2G328500, GRMZM2G162250, GRMZM2G114893, and GRMZM2G138949 were identified, which played important roles in the response of stress resilience and the regulation of plant growth and development. Genomic prediction (GP) accuracies observed in the GWAS panel and DH1 population were 0.61 and 0.51, respectively. This study provided new insight into the genetic architecture of quantitative resistance of common rust. In tropical maize, common rust could be improved by pyramiding the new sources of quantitative resistance through marker-assisted selection (MAS) or genomic selection (GS), rather than the implementation of MAS for the single dominant race-specific resistance gene.

Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning.

Many applications of plant pathology had been enabled by the evolution of artificial intelligence (AI). For instance, many researchers had used pre-trained convolutional neural networks (CNNs) such as the VGG-16, Inception, and Google Net to mention a few, for the classifications of plant diseases. The trend of using AI for plant disease classification has grown to such an extent that some researchers were able to use artificial intelligence to also detect their severities. The purpose of this study is to introduce a novel approach that is reliable in predicting severities of the maize common rust disease by CNN deep learning models. This was achieved by applying threshold-segmentation on images of diseased maize leaves (Common Rust disease) to extract the percentage of the diseased leaf area which was then used to derive fuzzy decision rules for the assignment of Common Rust images to their severity classes. The four severity classes were then used to train a VGG-16 network in order to automatically classify the test images of the Common Rust disease according to their classes of severity. Trained with images developed by using this proposed approach, the VGG-16 network achieved a validation accuracy of 95.63% and a testing accuracy of 89% when tested on images of the Common Rust disease among four classes of disease severity named Early stage, Middle stage, Late Stage and Healthy stage.

Pest categorisation of Puccinia pittieriana.

The Panel on Plant Health performed a pest categorisation of Puccinia pittieriana, the causal agent of common rust of potato, for the EU. The pathogen is a single taxonomic entity and reliable methods exist for its detection and identification. Cultivated potato (Solanum tuberosum) and tomato (Solanum lycopersicum) are the main hosts of P. pittieriana. Some wild solanaceous plants can also be affected by the pathogen. P. pittieriana is present in countries of South and Central America (most commonly at elevations of 3,000-4,000 m), but uncertainty exists about its presence in Bolivia and Paraguay. The pathogen is not known to occur in the EU and is listed in Annex IIAI of Directive 2000/29/EC. P. pittieriana could potentially enter the EU mainly on living host plants and infested soil attached to potato tubers originated in infested areas. Potato and tomato crops are widely distributed in the EU and the prevailing climatic conditions, at least in part of the risk assessment area, are suitable for the establishment and spread of the pathogen. There is uncertainty on the yield/quality losses currently caused by the pathogen in the infested areas. Nevertheless, it is expected that the introduction and spread of P. pittieriana in the EU could impact potato and tomato production, although the magnitude is unknown. Cultural practices and chemical measures may reduce the inoculum sources but they cannot eliminate the pathogen. Phytosanitary measures are available to mitigate the risk of introduction and spread of the pathogen in the EU. P. pittieriana meets all the criteria assessed by EFSA for consideration as a potential Union quarantine pest. As P. pittieriana is not known to occur in the EU, this criterion assessed by EFSA to consider it as a Union regulated non-quarantine pest is not met.