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This paper presents a novel approach to agricultural disease diagnostics through the integration of Deep Learning (DL) techniques with Visual Question Answering (VQA) systems, specifically targeting the detection of wheat rust. Wheat rust is a pervasive and destructive disease that significantly impacts wheat production worldwide. Traditional diagnostic methods often require expert knowledge and time-consuming processes, making rapid and accurate detection challenging. We drafted a new, WheatRustDL2024 dataset (7998 images of healthy and infected leaves) specifically designed for VQA in the context of wheat rust detection and utilized it to retrieve the initial weights on the federated learning server. This dataset comprises high-resolution images of wheat plants, annotated with detailed questions and answers pertaining to the presence, type, and severity of rust infections. Our dataset also contains images collected from various sources and successfully highlights a wide range of conditions (different lighting, obstructions in the image, etc.) in which a wheat image may be taken, therefore making a generalized universally applicable model. The trained model was federated using Flower. Following extensive analysis, the chosen central model was ResNet. Our fine-tuned ResNet achieved an accuracy of 97.69% on the existing data. We also implemented the BLIP (Bootstrapping Language-Image Pre-training) methods that enable the model to understand complex visual and textual inputs, thereby improving the accuracy and relevance of the generated answers. The dual attention mechanism, combined with BLIP techniques, allows the model to simultaneously focus on relevant image regions and pertinent parts of the questions. We also created a custom dataset (WheatRustVQA) with our augmented dataset containing 1800 augmented images and their associated question-answer pairs. The model fetches an answer with an average BLEU score of 0.6235 on our testing partition of the dataset. This federated model is lightweight and can be seamlessly integrated into mobile phones, drones, etc. without any hardware requirement. Our results indicate that integrating deep learning with VQA for agricultural disease diagnostics not only accelerates the detection process but also reduces dependency on human experts, making it a valuable tool for farmers and agricultural professionals. This approach holds promise for broader applications in plant pathology and precision agriculture and can consequently address food security issues. [ABSTRACT FROM AUTHOR]

In the absence of alternate hosts, the epidemiology of wheat rusts has remained elusive in many countries including India. This paper synthesises salient information from historical records collected at the Indian Institute of Wheat and Barley Research, from surveys of alternate and alternative hosts, and from observations in off‐season wheat disease monitoring nurseries. The primary source of rust inoculum to wheat in the plains of India was previously suggested to originate from off‐season wheat, self‐sown plants and green bridges in the hills. However, nowadays there is negligible cultivation of off‐season wheat in the hills, but still recurrence of wheat rust is a regular phenomenon in India. As observed previously, none of the Berberis spp. or grasses could be related to wheat rusts in this study. Thus, the wheat rust pathogens appear to multiply clonally in India. At present, new virulent pathotypes of Puccinia striiformis f. sp. tritici are identified initially from the plains of Punjab and other areas in the foothills of Himachal Pradesh, and after 6–7 years these pathotypes become predominant in the hills. Moreover, owing to the cultivation of traditional wheat germplasm, less virulent pathotypes of the stripe rust pathogen occur in the higher hills, and most of the present‐day wheat varieties are resistant to these pathotypes. The avirulence/virulence profiles of Indian pathotypes of Puccinia spp. on wheat indicate that our pathotype flora is different from that of adjoining countries and continents, which rules out their incursion from the adjoining countries. Wheat rust infection in the plains appears to be independent of the hills and movement of both leaf rust and stripe rust occurs both ways gradually year after year. It appears that the wheat rusts survive on grasses or other non‐gramineous hosts occurring locally near water bodies, forests, grasslands or damp areas in the hills and plains of India. [ABSTRACT FROM AUTHOR]

Abstract Background Wheat rusts are important biotic stresses, development of rust resistant cultivars through molecular approaches is both economical and sustainable. Extensive phenotyping of large mapping populations under diverse production conditions and high-density genotyping would be the ideal strategy to identify major genomic regions for rust resistance in wheat. The genome-wide association study (GWAS) population of 280 genotypes was genotyped using a 35 K Axiom single nucleotide polymorphism (SNP) array and phenotyped at eight, 10, and, 10 environments, respectively for stem/black rust (SR), stripe/yellow rust (YR), and leaf/brown rust (LR). Results Forty-one Bonferroni corrected marker-trait associations (MTAs) were identified, including 17 for SR and 24 for YR. Ten stable MTAs and their best combinations were also identified. For YR, AX-94990952 on 1A + AX-95203560 on 4A + AX-94723806 on 3D + AX-95172478 on 1A showed the best combination with an average co-efficient of infection (ACI) score of 1.36. Similarly, for SR, AX-94883961 on 7B + AX-94843704 on 1B and AX-94883961 on 7B + AX-94580041 on 3D + AX-94843704 on 1B showed the best combination with an ACI score of around 9.0. The genotype PBW827 have the best MTA combinations for both YR and SR resistance. In silico study identifies key prospective candidate genes that are located within MTA regions. Further, the expression analysis revealed that 18 transcripts were upregulated to the tune of more than 1.5 folds including 19.36 folds (TraesCS3D02G519600) and 7.23 folds (TraesCS2D02G038900) under stress conditions compared to the control conditions. Furthermore, highly expressed genes in silico under stress conditions were analyzed to find out the potential links to the rust phenotype, and all four genes were found to be associated with the rust phenotype. Conclusion The identified novel MTAs, particularly stable and highly expressed MTAs are valuable for further validation and subsequent application in wheat rust resistance breeding. The genotypes with favorable MTA combinations can be used as prospective donors to develop elite cultivars with YR and SR resistance.

Partial resistance to multiple biotrophic fungal pathogens in wheat (Triticum aestivum L.) is conferred by a variant of the Lr67 gene, which encodes a hexose–proton symporter. Two mutations (G144R and V387L) differentiate the resistant and susceptible protein variants (Lr67res and Lr67sus). Lr67res lacks sugar transport capability and was associated with anion transporter-like properties when expressed in Xenopus laevis oocytes. Here, we extended this functional characterization to include yeast and in planta studies. The Lr67res allele, but not Lr67sus , induced sensitivity to ions in yeast (including NaCl, LiCl, and KI), which is consistent with our previous observations that Lr67res expression in oocytes induces novel ion fluxes. We demonstrate that another naturally occurring single amino acid variant in wheat, containing only the Lr67G144R mutation, confers rust resistance. Transgenic barley plants expressing the orthologous HvSTP13 gene carrying the G144R and V387L mutations were also more resistant to Puccinia hordei infection. NaCl treatment of pot-grown adult wheat plants with the Lr67res allele induced leaf tip necrosis and partial leaf rust resistance. An Lr67res-like function can be introduced into orthologous plant hexose transporters via single amino acid mutation, highlighting the strong possibility of generating disease resistance in other crops, especially with gene editing. [ABSTRACT FROM AUTHOR]

This study proposes the relationship between fungal bioaerosols and biotic stress on crops using a case study on wheat leaf rust reported from northern India. We sampled and quantified the size-resolved fungal bioaerosols using the next-generation sequencing technique from a wheat crop field during winter. Puccinia recondita, the fungal pathogen that causes wheat leaf rust, was identified during the study period. The pathogen is known for its frequent and widespread occurrence of new variants that causes disease-resistant crop varieties susceptible to infections. This reveals the need for frequent and systematic monitoring to prevent rust infection. In the current study, the size-resolved fungal bioaerosol characterization was linked to the dispersal properties of the fungal propagules, and using a theoretical dispersion model, originating source and the areas of high risk for wheat leaf rust infection were identified. Our findings may serve as a vital reference for crop pathologists, agro technologists, environmentalists, and policymakers to expand the investigation on the biotic stress caused by the invasion of fungal bioaerosols on various crops and to implement preventive measures to ensure global food security. [ABSTRACT FROM AUTHOR]

Achieving durable resistance against wheat rust diseases is a primary goal of many wheat breeding programmes. This study aimed to characterize adult plant rust resistance in some old and new Indian bread-wheat varieties. Sixty-four bread wheat genotypes were evaluated to identify adult plant resistance (APR) to stripe rust and leaf rust during 2016-17 and 2017-18 under controlled environment. In these genotypes, APR was characterized using final disease score (FDS), average coefficient of infection (ACI), and area under disease progress curve (AUDPC). Majority of the genotypes were susceptible to moderately susceptible to stripe rust and moderately resistant to moderately susceptible to leaf rust. Leaf tip necrosis (LTN) and pseudo black chaff (PBC) were present in 41 and 33 genotypes, respectively. The three linked diagnostic molecular markers for APR genes, viz. Yr18/Lr34, Yr46/Lr67 and Lr68 were used to characterize the test genotypes and interestingly, 39 genotypes possessed at least one of the three resistance genes. None of the genotypes carried all the three genes. Lr68 was present in the highest frequency. Pre-green revolution tall Indian bread-wheat varieties, viz. NP101, NP836, NP850, NP856, NP876, and NP880 showed the presence of both Yr46/Lr67 and Lr68, whereas Yr18/Lr34 and Sr2/Yr30 were absent. Post-green revolution semi-dwarf wheats showed new combinations of APR with decreased frequency of Yr46/Lr67 and Lr68 genes. The results of this investigation suggest that the APR genes Yr46/Lr67 and Lr68 do not function additively. [ABSTRACT FROM AUTHOR]

Currently, the main methods for detecting plant diseases are sampling and manual visual inspection. However, these methods are time-consuming, laborious and prone to misinterpretation. Rapid advances in Deep Learning (DL) techniques offer new possibilities. This study focused on analysing the confounding factors among three types of wheat rust (stripe rust, leaf rust and stem rust) and aimed to achieve higher classification accuracy. The following approaches were used: (1) Images were collected from several crops and diseases: Wheat Rusts Dataset (WRD), Wheat Common Disease Dataset (WDD), and Common Poaceae Disease Dataset (PDD); (2) Seven common convolutional neural network (CNN) models were made and their performance compared. DenseNet121 was selected as the base model, and its classification results further analysed. The results of the above analyses were then considered using phenotypic morphology and model structure analysis, as well as potential confounder discussions; (3) Adjustments and optimisations were made based on the identified confounding factors. The final improved model, designated Imp-DenseNet, achieved the following accuracies with different datasets: Top-1 accuracy = 98.32% (WRD), Top-3 accuracy = 97.30% (WDD) and Top-5 accuracy = 96.60% (PDD) (Top-x Accuracy refers to the accuracy of the top-ranked category that matches or containing the actual results). The study revealed the potential factors contributing to the confusion among the three wheat rusts and successfully achieved higher accuracy. It can provide a new perspective for future research on other diseases of wheat or other crops. [Display omitted] • Confusion factors of wheat rust were analysed from different perspectives • Combine knowledges of plant protection, plant phenotype morphology analysis, DL and CV • Remodel DenseNet using attention mechanism, SPP, and factorisation thoughts • Verify validity of methods with Grad-CAM, Yolo v8 and Transformer [ABSTRACT FROM AUTHOR]

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The rust diseases, including leaf rust caused by Puccinia triticina (Pt), stem rust caused by P. graminis f. sp. tritici (Pgt), and stripe rust caused by P. striiformis f. sp. tritici (Pst), are major limiting factors in wheat production worldwide. Identification of novel sources of rust resistance genes is key to developing cultivars resistant to rapidly evolving pathogen populations. Aegilops longissima is a diploid wild grass native to the Levant and closely related to the modern bread wheat D subgenome. To explore resistance genes in the species, we evaluated a large panel of Ae. longissima for resistance to several races of Pt, Pgt, and Pst, and conducted a genome-wide association study (GWAS) to map rust resistance loci in the species. A panel of 404 Ae. longissima accessions, mostly collected from Israel, were screened for seedling-stage resistance to four races of Pt, four races of Pgt, and three races of Pst. Out of the 404 accessions screened, two were found that were resistant to all 11 races of the three rust pathogens screened. The percentage of all accessions screened that were resistant to a given rust pathogen race ranged from 18.5% to 99.7%. Genotyping-by-sequencing (GBS) was performed on 381 accessions of the Ae. longissima panel, wherein 125,343 single nucleotide polymorphisms (SNPs) were obtained after alignment to the Ae. longissima reference genome assembly and quality control filtering. Genetic diversity analysis revealed the presence of two distinct subpopulations, which followed a geographic pattern of a northern and a southern subpopulation. Association mapping was performed in the genotyped portion of the collection (n = 381) and in each subpopulation (n = 204 and 174) independently via a single-locus mixed-linear model, and two multi-locus models, FarmCPU, and BLINK. A large number (195) of markers were significantly associated with resistance to at least one of 10 rust pathogen races evaluated, nine of which are key candidate markers for further investigation due to their detection via multiple models and/or their association with resistance to more than one pathogen race. The novel resistance loci identified will provide additional diversity available for use in wheat breeding. [ABSTRACT FROM AUTHOR]

Ethiopia is the primary wheat producer in Sub-Saharan Africa (SSA) owing to the suitable agro-ecological conditions. Despite wheats economic potential for food security, the actual yield under smallholder farmers conditions is low due to various production constraints. Thus, the objectives of this study were to assess the present wheat production opportunities and constraints and identify farmer-preferred traits to guide variety design with stem rust-resistance and economic traits in eastern Ethiopia. Data on production constraints and trait preferences were collected using structured questionnaires involving 144 wheat-producing farmers. Wheat rust (reported by 97.3% of respondents), small land size (90.4%) and a lack of improved varieties (75.6%) were identified as the major constraints. About 41.7% of respondents in the West-Hararghe and 27.8% in the East-Hararghe zones did not use crop protection strategies to control rusts. Substantial respondent farmers used cultural practices (18.8%), rust-resistant cultivars (13.2%) or, a combination of these (10.4%) to control rust diseases. The essential farmer preferred traits in a wheat variety were rust resistance, high yield potential and good quality grain for bread making. Therefore, there is a need to breed new varieties with high grain yield and quality and durable rust resistance for sustainable wheat production in eastern Ethiopia.

Cysteine-rich secretory proteins (C), antigen 5 (A), and pathogenesis-related 1 proteins (P) comprise widespread CAP superfamily proteins, which have been proven to be novel virulence factors of mammalian pathogenic fungi and some plant pathogens. Despite this, the identification and function of CAP proteins in more species of plant pathogens still need to be studied. This work presents the identification and functional analysis of CAP superfamily proteins from Puccinia striiformis f. sp. tritici (Pst), an important fungal pathogen that causes wheat stripe rust on wheat worldwide. A total of six CAP genes were identified in the Pst genome, designated as PsCAP1–PsCAP6. Five PsCAP proteins, including PsCAP1, PsCAP2, PsCAP3, PsCAP4, and PsCAP5, have N-terminal signal peptides secreted with the yeast signal sequence trap assay. Single-nucleotide polymorphism (SNP) analysis indicated that they showed a low level of intraspecies polymorphism. The expression abundance of PsCAP genes at different Pst infection stages was detected by RT-qPCR, and most of them were highly expressed during Pst infection on wheat and also Pst sexual reproduction on barberry (Berberis shensiana). Noticeably, the silencing of these six PsCAP genes by BSMV-mediated HIGS indicated that PsCAP1, PsCAP4, and PsCAP5 contribute significantly to Pst infection in wheat. These results indicate that PsCAP proteins may act as virulence factors during Pst infection, which also provides insights into Pst pathogenicity. [ABSTRACT FROM AUTHOR]

The rust diseases, including leaf rust caused by Puccinia triticina (Pt), stem rust caused by P. graminis f. sp. tritici (Pgt), and stripe rust caused by P. striiformis f. sp. tritici (Pst), are major limiting factors in wheat production worldwide. Identification of novel sources of rust resistance genes is key to developing cultivars resistant to rapidly evolving pathogen populations. Aegilops longissima is a diploid wild grass native to the Levant and closely related to the modern bread wheat D subgenome. To explore resistance genes in the species, we evaluated a large panel of Ae. longissima for resistance to several races of Pt, Pgt, and Pst, and conducted a genome-wide association study (GWAS) to map rust resistance loci in the species. A panel of 404 Ae. longissima accessions, mostly collected from Israel, were screened for seedling-stage resistance to four races of Pt, four races of Pgt, and three races of Pst. Out of the 404 accessions screened, two were found that were resistant to all 11 races of the three rust pathogens screened. The percentage of all accessions screened that were resistant to a given rust pathogen race ranged from 18.5% to 99.7%. Genotyping-by-sequencing (GBS) was performed on 381 accessions of the Ae. longissima panel, wherein 125,343 single nucleotide polymorphisms (SNPs) were obtained after alignment to the Ae. longissima reference genome assembly and quality control filtering. Genetic diversity analysis revealed the presence of two distinct subpopulations, which followed a geographic pattern of a northern and a southern subpopulation. Association mapping was performed in the genotyped portion of the collection (n = 381) and in each subpopulation (n = 204 and 174) independently via a single-locus mixed-linear model, and two multi-locus models, FarmCPU, and BLINK. A large number (195) of markers were significantly associated with resistance to at least one of 10 rust pathogen races evaluated, nine of which are key candidate markers for further investigation due to their detection via multiple models and/or their association with resistance to more than one pathogen race. The novel resistance loci identified will provide additional diversity available for use in wheat breeding.

This review gives a perspective of selected advances made since the middle of the 20th century in plant disease modeling, and the associated increase in the number of models published during that time frame. This progress can be mainly atti-ibuted to advances in (i) sensors and automatic environmental data collection technology, (ii) instrumentation and methods for studying botanical epidemiology, and (iii) data analytics and computer science. We review the evolution of techniques for developing data-based (empirical) models and process-based (mechanistic) models using the wheat rusts as a case study. We also describe the increased importance of knowledge about biological processes for plant disease modeling by using apple scab as a second case study. For both wheat rusts and apple scab. we describe how the models have evolved over the last 50 years by considering certain milestones that have been achieved in disease modeling. Finally. we describe how plant disease models are used as part of a multi-modeling approach to develop decision-making tools in the application of integrated pest management. [ABSTRACT FROM AUTHOR]

Created in 1943, the Mexican Agricultural Program (MAP) was a collaborative program between the Rockefeller Foundation and the Mexican government aimed at improving yields of corn and wheat varieties in Mexico. The MAP’s wheat program was more influential than its corn breeding program, and wheat became the centerpiece of the Green Revolution beginning in the 1960s. This article reveals that the environmental origins of the MAP’s wheat program lay in combating a plant disease fungus commonly known as wheat rust, which harmed farmers in both northern Mexico and the US hard red spring wheat region. Rust outbreaks originated in US barberry bushes, so one might think that Mexican scientists would have entered the United States to solve this problem with transnational wheat rust. Instead, the reverse occurred. Nevertheless, the MAP successfully produced new rust-resistant wheat during the formative years of the MAP’s wheat program. Between 1943 and 1953, rust-resistant wheat helped increase Mexican wheat production 84 percent and yields 59 percent. Rust-resistant wheat offered an alternative model for a sustainable, resource-neutral green revolution. Rather than remain in their field of expertise, plant pathologists pivoted to semidwarf wheat and fertilizer use after 1953, a shift that defined the course of the Green Revolution. [ABSTRACT FROM AUTHOR]

Introduction One of the effective strategies to control wheat diseases is to use a mixture of varieties. Basically, varieties with good and similar agricultural traits, but different in diseases resistance should be used. It is common to use a mixture of cultivars, when the high-yielding cultivar is sensitive and the lowyielding cultivar is resistant, so that their mixture has an acceptable efficiency. The mixture of cultivars is defined as a mixture of different cultivars that are similar enough to grow together, but differ in many traits such as disease resistance. The mixture of cultivars does not completely stop the disease, instead it reduces the disease progression rate by eliminating a large number of pathogen spores in each reproductive cycle. The aim of this research was to investigate the effect of mixed cultivation of three cultivars, Kohdasht, Moghan and Morvarid, in controlling four diseases, powdery mildew, tan spot, stripe rust and leaf rust. Materials and methods To investigate the effect of the mixture of cultivars in control of wheat leaf diseases, a research was performed in a randomized complete block design with 12 treatments and four replications in the Gorgan Agricultural Research Station, Gorgan, Iran. The studied cultivars included three cultivars, Kohdasht, Moghan, and Maorvarid, and the evaluated diseases were four leaf diseases including powdery mildew, tan spot, stripe (yallow) rust, and leaf (brown) rust. Powdery mildew and tan spot were measured based on contamination rate using the Sari and Prescott scale, and atripe and leaf rusts were measured based on the disease type and severity. Final disease severity and standardized area under disease progression curve (SAUDPC) were evaluated for all four diseases. Research findings The results of the statistical analysis of data revealed that there was a very significant statistical difference (P<0.01) between experimental treatments for the final disease severity and SAUDPC of all four diseases. The results of comparison of means for powdery mildew showed that all treatments had a final disease severity higher than 30% and lower than 45%. Therefore, none of the treatments could reduce the severity of this disease. However, in term of stripe rust, the resistance of Kohdasht and Morvarid cultivars and their mixtures was clearly observed. For tan spot, the lowest final severity of disease was recorded in Morvarid and 1:1 mixture of Kohdasht-Moghan, and the other mixtures of Kohdasht-Moghan could not significantly reduce the severity of this disease. In term of leaf rust, the lowest final severity of the disease was observed in Morvarid and Moghan cultivars with a density of 150 seeds per m², and the highest final severity in Kohdast cultivar. Conclusion In this research, the effect of the mixture of cultivars in the control of wheat leaf diseases was investigated. In the case of powdery mildew, all treatments had a final severity of the disease higher than 30% and lower than 45%, and therefore, there is no proper efficiency in any of the treatments to reduce the severity of this disease. For yellow rust disease, the resistance of Kohdasht (treatment No. 1) and Marwarid (treatment No. 3) cultivars and their seed mixture (treatment No. 10) was clearly observed. In term of tan spot disease, the lowest final severity of the disease was observed in two treatments, Morvarid cultivar (treatment No. 3) and 1:1 mixture of Kohdasht and Mughan cultivars treatment No. 4), and the other proportions of the seed mixture of Kohdasht and Moghan varieties could not properly control the disease. In the case of brown rust disease, the lowest final intensity of the disease was observed in two treatments, Morvarid (treatment No. 3) and Moghan (treatment No. 12) with a density of 150 seeds per m², and the highest severity of this disease was observed in the treatments belonging to Kohdasht variety. [ABSTRACT FROM AUTHOR]

Wheat rust is one of the important factors leading to wheat yield decline. The traditional method of artificial identification of wheat rust remains inefficient. With the development of unmanned technology, unmanned aerial vehicles (UAVs) with advanced deep vision models can be used to monitor wheat growth, hence enabling timely disease detection and deployment of corresponding treatment measures. However, due to the limitation of embedded system hardware, it is a challenge to deploy a high‐accuracy and lightweight wheat rust detection model in an embedded system. In this paper, a training method based on transfer learning and sharpness‐aware minimization (SAM) is proposed to improve the accuracy of lightweight models. Specifically, the initial model is pretrained on an ImageNet dataset, and then the classifier of the model is fine‐tuned on the wheat rust training set; to prevent the risk of overfitting, the SAM method is applied to adjust the global parameters of the model. The experimental results on the Yellow‐Rust‐19 dataset show that the training method can effectively improve the accuracy of the wheat rust detection model, which is 3.56% higher than the existing methods. In addition, the parameter scale and computing resource requirements of four lightweight models are compared. The results indicate that MobileNetV3‐Small can achieve satisfactory accuracy with low requirements for storage and computing resource, and a detection frame rate of 101.36 frames per second in the Raspberry Pi, which is suitable for the unmanned detection of wheat rust. [ABSTRACT FROM AUTHOR]

In order to explore the feasibility of pyraclostrobin in preventing diseases of wheat at different growth stages, the virulences of pyraclostrobin against the causal agents of wheat sheath blight (Rhizoctonia cerealis), root rot (Bipolaris sorokiniana), total rot (Gaeumannomyces tritici), and head blight (Gibberella zeae) were determined by the mycelial growth rate method. Using the seed germination test, the effect of different concentrations of pyraclostrobin coating on wheat seed germination was determined. The control effects of pyraclostrobin 5% FS on wheat sheath blight in pots were determined by the bacteria-soil mixing method and the field control effects of pyraclostrobin 5% SC against R. cerealis, B. sorokiniana, G. tritici, and G. zeae were also determined. The results showed that the EC50 value of pyraclostrobin against R. cerealis, B. sorokiniana, G. tritici, and G. zeae were 0.404, 5.862, 0.193, and 1.372 mg/L, respectively. Different coating ratios of pyraclostrobin 5% FS had a certain promotion effect on the fresh weight and dry weight of Jimai 22 under the condition of 25 and showed a certain promotion effect on the germination potential, germination rate, germination index, and vigor index of wheat seeds under the condition of 10. The pot experiment showed that pyraclostrobin 5% FS had a control efficacy of more than 74% on wheat sheath blight 20 days after seedling, and the control efficacy reached 99.02% when the coating ratio was 1.50. The control efficacies of pyraclostrobin 5% SC on wheat powdery mildew, rust and scab were 100%, 100%, and 75.87% respectively under the dose of 3000 mL/hm2 with two sprays during early flowering and full flowering periods. The research showed that pyraclostrobin can be well applied to the control of wheat diseases in different growth stages. [ABSTRACT FROM AUTHOR]

Purpose. To investigate the possibility of diagnosing yellow and brown rust (Puccinia striiformis f. sp. tritici West. Puccinia triticina f. sp. tritici Erikss), yellow spot (Pyrenophora tritici-repentis (Died.) Drechsler) on wheat leaves from images using convolutional neural networks, to compare the most successful and compact neural network architectures. Materials and methods. The material for the research was the images of samples of wheat leaves affected by rust and spots, obtained in the conditions of infectious nurseries of the All-Russian Research Institute of Biological Plant Protection. The total sample size included 5169 images, including brown rust - 227, yellow rust – 1283, yellow spot – 3659. Research methods: data preprocessing methods, training methods for convolutional neural networks. Results. A comparison was made of the four most successful and compact neural network architectures GoogleNet, ResNet-18, SqueezeNet-1.0 and DenseNet-121. On test data – 518 images, all of the above models demonstrated high prediction quality. The best result was shown by DenseNet-121, providing a classification accuracy of over 99 %, with two false positives. Conclusion. The possibility of diagnosing wheat fungal diseases from images using modern methods of computer vision was analyzed. It is shown that under controlled conditions, with a competent organization of the process of collecting and marking data, the problem is successfully solved, and the listed models are the reserve that will automate the stage of phytosanitary monitoring diagnostics.

Wheat leaf rust, caused by Puccinia triticina Eriks. (Pt), is a global wheat disease threatening wheat production. Dissecting how Pt effector proteins interact with wheat has great significance in understanding the pathogenicity mechanisms of Pt. In the study, the cDNA of Pt 13-5-72 interacting with susceptible cultivar Thatcher was used as template to amplify Pt13024 gene. The expression pattern and structure of Pt13024 were analyzed by qRT-PCR and online softwares. The secretion function of Pt13024 signal peptide was verified by the yeast system. Subcellular localization of Pt13024 was analyzed using transient expression on Nicotiana benthamiana. The verification that Pt13024 inhibited programmed cell death (PCD) was conducted on N. benthamiana and wheat. The deletion mutation of Pt13024 was used to identify the virulence function motif. The transient transformation of wheat mediated by the type III secretion system (TTSS) was used to analyze the activity of regulating the host defense response of Pt13024. Pt13024 gene silencing was performed by host-induced gene silencing (HIGS). The results showed that Pt13024 was identified as an effector and localized in the cytoplasm and nucleus on the N. benthamiana. It can inhibit PCD induced by the Bcl-2-associated X protein (BAX) from mice and infestans 1 (INF1) from Phytophthora infestans on N. benthamiana, and it can also inhibit PCD induced by DC3000 on wheat. The amino acids 22 to 41 at N-terminal of the Pt13024 are essential for the inhibition of programmed cell death (PCD) induced by BAX. The accumulation of reactive oxygen species and deposition of callose in near-isogenic line TcLr30, which is in Thatcher background with Lr30, induced by Pt13024 was higher than that in 41 wheat leaf rust-resistant near-isogenic lines (monogenic lines) with different resistance genes and Thatcher. Silencing of Pt13024 reduced the leaf rust resistance of Lr30 during the interaction between Pt and TcLr30. We can conclude that Pt13024 is avirulent to TcLr30 when Pt interacts with TcLr30. These findings lay the foundation for further investigations into the role of Pt effector proteins in pathogenesis and their regulatory mechanisms. [ABSTRACT FROM AUTHOR]

Rust is a common disease in wheat that significantly impacts its growth and yield. Stem rust and leaf rust of wheat are difficult to distinguish, and manual detection is time-consuming. With the aim of improving this situation, this study proposes a method for identifying wheat rust based on ensemble learning (WR-EL). The WR-EL method extracts and integrates multiple convolutional neural network (CNN) models, namely VGG, ResNet 101, ResNet 152, DenseNet 169, and DenseNet 201, based on bagging, snapshot ensembling, and the stochastic gradient descent with warm restarts (SGDR) algorithm. The identification results of the WR-EL method were compared to those of five individual CNN models. The results show that the identification accuracy increases by 32%, 19%, 15%, 11%, and 8%. Additionally, we proposed the SGDR-S algorithm, which improved the f1 scores of healthy wheat, stem rust wheat and leaf rust wheat by 2%, 3% and 2% compared to the SGDR algorithm, respectively. This method can more accurately identify wheat rust disease and can be implemented as a timely prevention and control measure, which can not only prevent economic losses caused by the disease, but also improve the yield and quality of wheat. [ABSTRACT FROM AUTHOR]

Abstract NPR1 is a key regulator of systemic acquired resistance (SAR) in plant species. In our previous study, we identified a conserved fungal effector PNPi from Puccinia striiformis f. sp. tritici (Pst) that can suppress acquired resistance in local leaf by directly targeting the wheat NPR1 protein. In this investigation, we identified and validated a novel protein interaction between PNPi and wheat pathogenesis-related TaPR1a in the apoplastic space. TaPR1a-overexpressing wheat lines exhibited enhanced resistance to both Pst and Puccinia triticina (Pt). We further determined that exogenous expression of PNPi RNA in transgenic wheat lines reduced the degree of acquired resistance to Magnaporthe oryzae isolate P131 in the region adjacent to Pseudomonas syringae pv. tomato DC3000 infection area. Additionally, when PNPi was overexpressed, the expression levels of two plant defense responsive genes were suppressed upon P. syringae DC3000 infection in the local infiltration region. These findings established the mechanism of a single rust effector that can suppress multiple defense responses in wheat plants by targeting different components.

Ethiopia is the primary wheat producer in Sub-Saharan Africa (SSA) owing to the suitable agro-ecological conditions. Despite wheats economic potential for food security, the actual yield under smallholder farmers conditions is low due to various production constraints. Thus, the objectives of this study were to assess the present wheat production opportunities and constraints and identify farmer-preferred traits to guide variety design with stem rust-resistance and economic traits in eastern Ethiopia. Data on production constraints and trait preferences were collected using structured questionnaires involving 144 wheat-producing farmers. Wheat rust (reported by 97.3% of respondents), small land size (90.4%) and a lack of improved varieties (75.6%) were identified as the major constraints. About 41.7% of respondents in the West-Hararghe and 27.8% in the East-Hararghe zones did not use crop protection strategies to control rusts. Substantial respondent farmers used cultural practices (18.8%), rust-resistant cultivars (13.2%) or, a combination of these (10.4%) to control rust diseases. The essential farmer preferred traits in a wheat variety were rust resistance, high yield potential and good quality grain for bread making. Therefore, there is a need to breed new varieties with high grain yield and quality and durable rust resistance for sustainable wheat production in eastern Ethiopia. [ABSTRACT FROM AUTHOR]

When the nitrogen absorption at mature period of wheat cultivar “Kinuakari” exceeded 16 kg/10a, the development of wheat rust at milk-ripe stage tended to increase. It was important to reduce the development of wheat rust on the flag leaves, because the development of wheat rust on the flag leaves at milk-ripe stage reduce wheat yield. It was effective to control wheat rust by using bactericides, propiconazole, metconazole, tebuconazole or azoxystrobin, from about 10 days after jointing stage to the flag leaf emergence stage (about 10 days before heading time) and during flowering period. [ABSTRACT FROM AUTHOR]

Plant disease has long been one of the major threats to world food security due to reduction in the crop yield and quality. Accurate and precise diagnosis of plant diseases has been a significant challenge. Cost-effective automated computational systems for disease diagnosis would facilitate advancements in agriculture. The objective of this paper is to explore computer vision based Artificial Intelligence method for automating the identification of yellow rust disease and improve the accuracy of plant disease identification. The dataset of 2000 images of wheat leaf were collected in the real life experimental conditions of ICAR-Indian Agricultural Research Institute, New Delhi in the crop season during January-April, 2019. Based on our experiment, we propose a deep learning-based approach to detect healthy leaves and yellow rust infected leaves in the wheat crop. The experiments are implemented in python with PyCharm IDE, utilizing the Keras deep learning library backend with TensorFlow. The proposed model achieves 97.3% testing accuracy and 98.42% as the training accuracy. The accuracy of the developed model can be improved further by training it with larger size of the dataset in future. In future, accuracy of computer vision based AI models can be improved by using the larger size training datasets. Also, these models can be used for providing automatic advisory services to the farmers, thereby, adding much needed assistance to the overloaded extension experts.

Stripe rust, caused by Puccinia striiformis f.sp. tritici (Pst), results in reduced yield and grain quality substantially, and increases production expenses, leading to billions of dollars in economic damages globally every year. Understanding the lifecycle of pathogens is vital in formulating effective management strategies for plant diseases. Pst has a complex life cycle that entails both sexual and asexual reproduction, facilitating the rapid evolution of new strains. The evolution of new races is primarily driven by the selection pressure imposed by the deployment of resistance genes in wheat cultivars. Work documented by prominent researchers conducted in diverse regions of the planet has been reviewed in this article to understand how disease perpetuates from one year to next year. Recent studies have shown that Pst populations can undergo rapid adaptation to changing environmental conditions such as temperature and humidity, which can influence the timing and severity of stripe rust epidemics. The evolution and diversity of Pst pose significant challenges for the management and control of stripe rust in wheat crops. Effective strategies for disease control require a better understanding of the genetic and geographic diversity of Pst populations and the factors driving their evolution. Effective monitoring of Pst populations is critical for developing strategies to control stripe rust and ensure the long-term sustainability of wheat production. Pathogenicity surveys, which involve testing Pst samples against differential host plants, and molecular techniques such as PCR and DNA sequencing are discussed as effective tools for tracking Pst populations, identifying new introductions and monitoring their evolution over time. A combination of traditional and molecular techniques has generated valuable data for understanding populations virulence of Pst. [ABSTRACT FROM AUTHOR]

Exploiting genetically diverse lines to identify genes for improving crop performance is needed to ensure global food security. A genome-wide association study (GWAS) was conducted using 46,268 SNP markers on a diverse panel of 143 hexaploid bread and synthetic wheat to identify potential genes/genomic regions controlling agronomic performance (yield and 26 yield-related traits), disease resistance, and grain quality traits. From phenotypic evaluation, we found large genetic variation among the 35 traits and recommended five lines having a high yield, better quality, and multiple disease resistance for direct use in a breeding program. From a GWAS, we identified a total of 243 significant marker-trait associations (MTAs) for 35 traits that explained up to 25% of the phenotypic variance. Of these, 120 MTAs have not been reported in the literature and are potentially novel MTAs. In silico gene annotation analysis identified 116 MTAs within genes and of which, 21 MTAs were annotated as a missense variant. Furthermore, we were able to identify 23 co-located multi-trait MTAs that were also phenotypically correlated to each other, showing the possibility of simultaneous improvement of these traits. Additionally, most of the co-located MTAs were within genes. We have provided genomic fingerprinting for significant markers with favorable and unfavorable alleles in the diverse set of lines for developing elite breeding lines from useful trait-integration. The results from this study provided a further understanding of genetically complex traits and would facilitate the use of diverse wheat accessions for improving multiple traits in an elite wheat breeding program.

Abstract Background Effective disease management depends on timely and accurate diagnosis to guide control measures. The capacity to distinguish between individuals in a pathogen population with specific properties such as fungicide resistance, toxin production and virulence profiles is often essential to inform disease management approaches. The genomics revolution has led to technologies that can rapidly produce high-resolution genotypic information to define individual variants of a pathogen species. However, their application to complex fungal pathogens has remained limited due to the frequent inability to culture these pathogens in the absence of their host and their large genome sizes. Results Here, we describe the development of Mobile And Real-time PLant disEase (MARPLE) diagnostics, a portable, genomics-based, point-of-care approach specifically tailored to identify individual strains of complex fungal plant pathogens. We used targeted sequencing to overcome limitations associated with the size of fungal genomes and their often obligately biotrophic nature. Focusing on the wheat yellow rust pathogen, Puccinia striiformis f.sp. tritici (Pst), we demonstrate that our approach can be used to rapidly define individual strains, assign strains to distinct genetic lineages that have been shown to correlate tightly with their virulence profiles and monitor genes of importance. Conclusions MARPLE diagnostics enables rapid identification of individual pathogen strains and has the potential to monitor those with specific properties such as fungicide resistance directly from field-collected infected plant tissue in situ. Generating results within 48 h of field sampling, this new strategy has far-reaching implications for tracking plant health threats.

Leaf, stem and stripe rust caused by Puccinia triticina, P. graminis f. sp. tritici and P. striiformis f. sp. tritici, respectively, are common wheat diseases. The deployment of complex rust resistance sources is likely to contribute to their durability. In this study, wheat varieties and experimental plants (F2), with different rust resistance gene combinations, were characterised using molecular markers, phenotypic evaluation and quantification of fungal biomass accumulation. The experimental wheat plants were developed from six donor parental varieties that in combination carry seven known rust resistance genes/quantitative trait loci (QTL). A timeline study revealed that quantification of fungal biomass accumulation for the rusts, 10‐day postinoculation (dpi), can successfully distinguish between resistant and susceptible wheat varieties. Phenotypic evaluation included seedling infection types and adult‐plant response types that were determined 15 dpi. Data obtained from the three methods were in support of each other and in combination successfully distinguished between rust‐resistant and susceptible experimental wheat plants. Resistance genes/QTL evaluated in this study, when deployed in combination, may lower the risk of rust outbreaks. [ABSTRACT FROM AUTHOR]

The wheat-rust pathosystem has been well-studied among host–pathogen interactions since last century due to its economic importance. Intensified efforts toward cloning of wheat rust resistance genes commenced in the late 1990s with the first successful isolation published in 2003. Currently, a total of 24 genes have been cloned from wheat that provides resistance to stem rust, leaf rust, and stripe rust. Among them, more than half (15) were cloned over the last 4 years. This rapid cloning of resistance genes from wheat can be largely credited to the development of approaches for reducing the genome complexity as 10 out of the 15 genes cloned recently were achieved by approaches that are summarized as TEnSeq (Target-sequence Enrichment and Sequencing) pipelines in this review. The growing repertoire of cloned rust resistance genes provides new tools to support deployment strategies aimed at achieving durable resistance. This will be supported by the identification of genetic variation in corresponding Avr genes from rust pathogens, which has recently begun. Although developed with wheat resistance genes as the primary targets, TEnSeq approaches are also applicable to other classes of genes as well as for other crops with complex genomes.

The wheat-rust pathosystem has been well-studied among host–pathogen interactions since last century due to its economic importance. Intensified efforts toward cloning of wheat rust resistance genes commenced in the late 1990s with the first successful isolation published in 2003. Currently, a total of 24 genes have been cloned from wheat that provides resistance to stem rust, leaf rust, and stripe rust. Among them, more than half (15) were cloned over the last 4 years. This rapid cloning of resistance genes from wheat can be largely credited to the development of approaches for reducing the genome complexity as 10 out of the 15 genes cloned recently were achieved by approaches that are summarized as TEnSeq (Target-sequence Enrichment and Sequencing) pipelines in this review. The growing repertoire of cloned rust resistance genes provides new tools to support deployment strategies aimed at achieving durable resistance. This will be supported by the identification of genetic variation in corresponding Avr genes from rust pathogens, which has recently begun. Although developed with wheat resistance genes as the primary targets, TEnSeq approaches are also applicable to other classes of genes as well as for other crops with complex genomes. [ABSTRACT FROM AUTHOR]

Timely and accurate disease incidence monitoring and estimation using satellite imagery is critical for the effective control of wheat rust disease. Though studies have been conducted using different spectral and sensing technologies to detect and monitor the wheat rust disease, few studies have been conducted in the application of high resolution multi-spectral satellite imagery in the monitoring of wheat rust to facilitate an operational monitoring over large areas. Since there are not many options for the multispectral data than the hyperspectral data, it is more important to select appropriate vegetation indices for the classification model. However, there have been few literatures about the comparison of these feature selection methods on the application of wheat disease. We proposed a wheat rust disease mapping protocol including removing the non-vegetated and lodged area, calculating and filtering the vegetation indices and mapping the wheat rust. In the process of filtering the proper features, we applied wrapper feature selection instead of traditional filter feature selection combined with the classification methods (support vector machine and random forests). The experiment data was a scene of ZY-3 satellite image of the wheat field in Changge County in Henan Province with a certain portion of rust disease. The classification results can achieve overall accuracy of higher than 90%, ranging from 90.80% to 95.10%. The wrapper feature selection method with the overall accuracy of 93.60% is better than filters feature selection method with the overall accuracy of 92.65%. The random forests method with the overall accuracy of 94.80% is better than support vector machine method with the overall accuracy of 91.45%. The high accuracies thus justified the feasibility of using high-resolution multi-spectral satellite images for mapping wheat rust disease, which is promising for this technology to be applied in the practical wheat production management. [ABSTRACT FROM AUTHOR]

Breeding crops for disease resistance is a sustainable approach to reduce yield losses. While significant research on the adoption and impacts of improved crop varieties exists, most studies have analyzed yield effects in general without distinguishing between different varietal traits and characteristics. Here, panel data from wheat farmers in Ethiopia were used to compare improved varieties that are resistant to stripe rust (caused by Puccinia striiformis f. sp . tritici) with improved susceptible and traditional susceptible varieties. Production function estimates suggest that improved resistant varieties raise effective yields by 8% in comparison to local susceptible varieties. The yield difference between improved resistant and improved susceptible varieties is positive but small because rust levels were not very high in the years under study. However, under drought and other abiotic stresses, improved varieties - with and without resistance to stripe rust - performed notably worse than local varieties. The worse performance under abiotic stress may explain why many farmers have recently switched back to growing traditional varieties. Sustainable adoption needs a combination of various traits in the same varieties, including high yield potential, grain quality, disease resistance and tolerance to drought and other production stresses. [ABSTRACT FROM AUTHOR]

Present study was aimed to screening the population of 25 wheat genotypes from Baluchistan region of Pakistan along with five commercial cultivars for leaf rust adult plant resistance (APR) through gene postulation using natural inoculation of Puccinia triticina Erikss local pathotype. Infection severity was recorded on scale in comparison with susceptible control "Morroco" cultivar. On the basis of phenotypic score, seven accessions and four varieties (Zardana-89, Sariab-92, Zarlashta-99 and Raskoh-05) with AUDPC values up to 20% were characterized as resistant genotypes. Coefficient of infection (CI) score ranged from 0-10 for some accessions and cultivars showing high level of adult plant resistance. Furthermore, bi-allelic STS marker csLV34 having close linkage with Lr34 (0.4cM). This marker amplified one gene specific allele of 150bp in 21 genotypes, including 19 accessions and two commercial varieties (Sariab-92 and Zarghoon-79) which confirmed presence of Lr34 gene conferring adult plant resistance against leaf rust. The rust pathogenicity scale varied for accessions from resistant to moderately susceptible. However, beside Lr34, phenotypic gene postulation, in combination with marker assisted selection for leaf rust resistance, has revealed presence of some other unknown resistance genes in local wheat germplasm which signified its use in wheat improvement programs both locally and abroad.

Wheat (Triticum aestivum L.) rusts are the most destructive and widespread among all other diseases of wheat because of their wide distribution, and their capacity to form new races that can attack previously resistant cultivars which result in serious yield losses. The molecular characterization and genetic diversity of 20 wheat genotypes was investigated using 34 polymorphic Simple Sequence Repeats (SSR) screened primers. About thirty-one loci were found. Lr-19 gene was present in all 20 wheat genotypes that cause resistance against wheat rust. Shalimar-86 and Chakwal-86 showed the highest genetic diversity with SH-02 and Ufaq respectively, giving a 98.94% genetic similarity and a minimum genetic diversity was observed between Chakwal-50 and Bhakar which showed that they are 74% similar. The current research found that SSR makers could distinguish and characterize all of the genotypes, more screened primers could be used for study and for saturation of different regions in further research. The identification of rust resistant genes in Pakistani wheat germplasm will help in accelerating the breeding program in future, including pyramiding of different wheat resistant genes in wheat genotypes and varieties.

Two classes of genes are used for breeding rust resistant wheat. The first class, called R (for resistance) genes, are pathogen race-specific in their action, effective at all plant growth stages and probably mostly encode immune receptors of the nucleotide binding leucine rich repeat (NB-LRR) class. The second class called Adult Plant Resistance genes (APR) because resistance is usually functional only in adult plants, and, in contrast to most R genes, the levels of resistance conferred by single APR genes are only partial and allow considerable disease development. Some but not all APR genes provide resistance to all isolates of a rust pathogen species and a subclass of these provides resistance to several fungal pathogen species. Initial indications are that APR genes encode a more heterogeneous range of proteins than R proteins. Two APR genes, Lr34 and Yr36, have been cloned from wheat and their products are an ABC transporter and a protein kinase, respectively. Lr34 and Sr2 have provided long lasting and widely used (durable) partial resistance and are mainly used in conjunction with other R and APR genes to obtain adequate rust resistance. We caution that some APR genes indeed include race-specific, weak R genes which may be of the NB-LRR class. A research priority to better inform rust resistance breeding is to characterize further APR genes in wheat and to understand how they function and how they interact when multiple APR and R genes are stacked in a single genotype by conventional and GM breeding. An important message is do not be complacent about the general durability of all APR genes.