Disease Of Maize Research Articles

Unveiling the biocontrol potential of Pseudomonas syringae through seed biopriming against charcoal rot disease in maize

In Punjab, Pakistan, maize production faces a significant threat from charcoal rot disease, caused by the soil-borne fungus Macrophomina phaseolina. To mitigate this issue and ensure food safety, researchers explored the potential of Pseudomonas syringae, a harmless gram-negative bacterium against charcoal rot disease in maize. Maize seeds treated with P. syringae (PS) were planted in soil containing the charcoal rot fungus in pot experiments. The outcome was then compared with the impact of NPK fertilizers and mancozeb fungicide. The positive control, with M. phaseolina alone, exhibited high disease severity resulting in a greater reduction of 30–70 % in the plant's biophysical and biochemical traits. Mancozeb, NPK, or PS, managed 66, 41, and 55 % of diseases and improved the biophysical and biochemical traits of the plants by up to 200 %. Applying P. syringae in combination with the recommended dose of NPK was found as the most promising treatment which significantly improved plant resistance by managing 90 % of disease through improving growth and biochemical traits (up to 300 %). This research suggests effective control of M. phaseolina by combining P. syringae with NPK, enhancing plant defense mechanisms and potentially improving pathogen-induced resistance in the maize plants.

Integrated analysis of transcriptomics and defense-related phytohormones to discover hub genes conferring maize Gibberella ear rot caused by Fusarium Graminearum

BackgroundGibberella ear rot (GER) is one of the most devastating diseases in maize growing areas, which directly reduces grain yield and quality. However, the underlying defense response of maize to pathogens infection is largely unknown.ResultsTo gain a comprehensive understanding of the defense response in GER resistance, two contrasting inbred lines ‘Nov-82’ and ‘H10’ were used to explore transcriptomic profiles and defense-related phytohormonal alterations during Fusarium graminearum infection. Transcriptomic analysis revealed 4,417 and 4,313 differentially expressed genes (DEGs) from the Nov-82 and H10, respectively, and 647 common DEGs between the two lines. More DEGs were obviously enriched in phenylpropanoid biosynthesis, secondary metabolites biosynthesis, metabolic process and defense-related pathways. In addition, the concentration of the defense-related phytohormones, jasmonates (JAs) and salicylates (SAs), was greatly induced after the pathogen infection. The level of JAs in H10 was more higher than in Nov-82, whereas an opposite pattern for the SA between the both lines. Integrated analysis of the DEGs and the phytohormones revealed five vital modules based on co-expression network analysis according to their correlation. A total of 12 hub genes encoding fatty acid desaturase, subtilisin-like protease, ethylene-responsive transcription factor, 1-aminocyclopropane-1-carboxylate oxidase, and sugar transport protein were captured from the key modules, indicating that these genes might play unique roles in response to pathogen infection,ConclusionsOverall, our results indicate that large number DEGs related to plant disease resistance and different alteration of defensive phytohormones were activated during F. graminearum infection, providing new insight into the defense response against pathogen invasion, in addition to the identified hub genes that can be further investigated for enhancing maize GER resistance.

Tip of the iceberg? Three novel TOPLESS-interacting effectors of the gall-inducing fungus Ustilago maydis.

Ustilago maydis is a biotrophic pathogen causing smut disease in maize. It secretes a cocktail of effector proteins, which target different host proteins during its biotrophic stages in the host plant. One such class of proteins we identified previously is TOPLESS (TPL) and TOPLESS-RELATED (TPR) transcriptional corepressors. Here, we screened 297 U. maydis effector candidates for their ability to interact with maize TPL protein RAMOSA 1 ENHANCER LOCUS 2 LIKE 2 (RELK2) and their ability to induce auxin signaling and thereby identified three novel TPL-interacting protein effectors (Tip6, Tip7, and Tip8). Structural modeling and mutational analysis allowed the identification of TPL-interaction motifs of Tip6 and Tip7. In planta interaction between Tip6 and Tip7 with RELK2 occurs mainly in nuclear compartments, whereas Tip8 colocalizes with RELK2 in a compartment outside the nucleus. Overexpression of Tip8 in nonhost plants leads to cell death, indicating recognition of the effector or its activity. By performing infection assays with single and multideletion mutants of U. maydis, we demonstrate a positive role of Tip6 and Tip7 in U. maydis virulence. Transcriptional profiling of maize leaves infected with Tip effector mutants in comparison with SG200 strain suggests Tip effector activities are not merely redundant.

Quantitative trait locus analysis of gray leaf spot resistance in the maize IBM Syn10 DH population.

The exploration and dissection of a set of QTLs and candidate genes for gray leaf spot disease resistance using two fully assembled parental genomes may help expedite maize resistance breeding. The fungal disease of maize known as gray leaf spot (GLS), caused by Cercospora zeae-maydis and Cercospora zeina, is a significant concern in China, Southern Africa, and the USA. Resistance to GLS is governed by multiple genes with an additive effect and is influenced by both genotype and environment. The most effective way to reduce the cost of production is to develop resistant hybrids. In this study, we utilized the IBM Syn 10 Doubled Haploid (IBM Syn10 DH) population to identify quantitative trait loci (QTLs) associated with resistance to gray leaf spot (GLS) in multiple locations. Analysis of seven distinct environments revealed a total of 58 QTLs, 49 of which formed 12 discrete clusters distributed across chromosomes 1, 2, 3, 4, 8 and 10. By comparing these findings with published research, we identified colocalized QTLs or GWAS loci within eleven clustering intervals. By integrating transcriptome data with genomic structural variations between parental individuals, we identified a total of 110 genes that exhibit both robust disparities in gene expression and structural alterations. Further analysis revealed 19 potential candidate genes encoding conserved resistance gene domains, including putative leucine-rich repeat receptors, NLP transcription factors, fucosyltransferases, and putative xyloglucan galactosyltransferases. Our results provide a valuable resource and linked loci for GLS marker resistance selection breeding in maize.

Prediction of the global occurrence of maize diseases and estimation of yield loss under climate change.

Climate change significantly impacts global maize production via yield reduction, posing a threat to global food security. Disease-related crop damage reduces quality and yield and results in economic losses. However, the occurrence of diseases caused by climate change, and thus crop yield loss, has not been given much attention. This study aims to investigate the potential impact of six major diseases on maize yield loss over the next 20 to 80 years under climate change. To this end, the Maximum Entropy model was implemented, based on Coupled Model Intercomparison Project 6 data. The results indicated that temperature and precipitation are identified as primary limiting factors for disease onset. Southern corn rust was projected to be the most severe disease in the future; with a few of the combined occurrence of all the selected diseases covered in this study were predicted to progressively worsen over time. Yield losses caused by diseases varied per continent, with North America facing the highest loss, followed by Asia, South America, Europe, Africa, and Oceania. This study provides a basis for regional projections and global control of maize diseases under future climate conditions. © 2024 Society of Chemical Industry.

Setosphaeria turcica, the Maize Leaf Blast Pathogen: Current Status and Infection Mechanisms

The fungal pathogen Setosphaeria turcica causes northern corn leaf blight (NCLB) in maize, sorghum, and related grasses. NCLB is a serious fungal foliar disease of cultivated maize that causes devastating yield losses. S. turcica infects maize leaves by means of a specialized cell, the appressorium, but the regulatory mechanisms that underlie appressorium-mediated infection remain largely unknown. Many regulatory pathways and a large number of genes have been described in S. turcica, and many of these genes have been cloned. Characterization of such disease-related genes is important for understanding the biological mechanisms of interaction between pathogen and host and can guide the development of strategies for disease control. There is a significant level of concern regarding the possible dissemination of the S. turcica pathogen to regions where NCLB is not presently prevalent. This scenario is of considerable concern and necessitates immediate research intervention. The present review brings together information on the epidemiology and infection mechanisms of S. turcica.

MC-ShuffleNetV2: A lightweight model for maize disease recognition

Maize has a long history of cultivation and is renowned for its high yield, superior quality, and adaptability. Currently, maize holds a significant position in grain cultivation and occupies a significant place in the agricultural structure. However, maize is susceptible to various diseases during its growth process, which can have a significant impact on the quality and yield. Traditional machine learning is heavily reliant on feature extraction, whereas deep learning has demonstrated notable success in image recognition for computer vision.The use of bloated models and the resulting wastage of computational resources represent significant challenges. The paper proposes a lightweight model, MC-ShuffleNetV2 (Mish + Convolutional Block Attention Module + ShuffleNetV2), to meet the practical needs of convolutional neural networks in maize disease image recognition. The model has designed with a focus on network lightweighting and accurate feature extraction. The model was constructed upon the foundation of the high-performance ShuffleNetV2 1 × network. The Convolutional Block Attention Module was integrated into the network architecture to enhance the model’s adaptive expressiveness. The depthwise separable convolution kernel of the depth-separable module was modified from a 3 × 3 kernel to a 5 × 5 kernel. This modification was implemented with the objective of expanding the image receptive field and extracting more detailed features of the image. It was necessary to modify the activation function in each stage for Mish. The model was compressed through the application of pruning operations. In the maize disease dataset test, the accuracy of the test set recognition accuracy of the network model constructed in this paper reaches 99.86 %, the model parameters are only 873,936, and the FLOPs (Floating-point Operations) are only 1,751,286. Compared with LeNet, AlexNet, MobileNetV2, and EfficientNetV2 models, the MC-ShufflenetV2 model’s recognition ability and size have obvious advantages, and it is more conducive to the actual deployment of the agricultural mobile terminal.

Mask-guided dual-perception generative adversarial network for synthesizing complex maize diseased leaves to augment datasets

In practice, acquiring and annotating data in specialized domains can be costly, thereby constraining the performance and applicability of deep learning. Utilizing generative models to synthesize data proves to be an effective augmentation technique. Therefore, this research proposes a diseased leaf generation pipeline to diversify the maize disease datasets. We introduce the Dual-Perception Cycle-Consistent Generative Adversarial Network (DP-CycleGAN). During training, incorporating our proposed Structure Perception (SP) loss and Texture Perception (TP) loss functions. These losses guide the model's attention areas through activation reconstruction and mask mechanisms, thereby improving the overall perceptual quality of the generated images and the realism of the disease lesions. We constructed a maize leaf mixed disease dataset to simulate the complex conditions of real-world disease occurrence. Experimental results show that the DP-CycleGAN generates higher-quality and more realistic diseased leaf images. Compared to CycleGAN and state-of-the-art method, DP-CycleGAN shows a 29.6% and 15.7% reduction in Fréchet Inception Distance (FID) scores and a 125.3% and 61.5% increase in Structural Similarity (SSIM) values, respectively. Simultaneously, by incorporating synthetic data during training, our approach significantly enhances the performance of the recognition model in scenarios of both data abundance and scarcity, with improvement rates exceeding two times those of existing state-of-the-art methods. This contributes to the application of Artificial Intelligence (AI) in agricultural production practices.

A High-Precision Identification Method for Maize Leaf Diseases and Pests Based on LFMNet under Complex Backgrounds.

Maize, as one of the most important crops in the world, faces severe challenges from various diseases and pests. The timely and accurate identification of maize leaf diseases and pests is of great significance for ensuring agricultural production. Currently, the identification of maize leaf diseases and pests faces two key challenges: (1) In the actual process of identifying leaf diseases and pests, complex backgrounds can interfere with the identification effect. (2) The subtle features of diseases and pests are difficult to accurately extract. To address these challenges, this study proposes a maize leaf disease and pest identification model called LFMNet. Firstly, the localized multi-scale inverted residual convolutional block (LMSB) is proposed to perform preliminary down-sampling on the image, preserving important feature information for the subsequent extraction of fine disease and pest features in the model structure. Then, the feature localization bottleneck (FLB) is proposed to improve the model's ability to focus on and locate disease and pest characteristics and to reduce interference from complex backgrounds. Subsequently, the multi-hop local-feature fusion architecture (MLFFA) is proposed, which effectively addresses the problem of extracting subtle features by enhancing the extraction and fusion of global and local disease and pest features in images. After training and testing on a dataset containing 19,451 images of maize leaf diseases and pests, the LFMNet model demonstrated excellent performance, with an average identification accuracy of 95.68%, a precision of 95.91%, a recall of 95.78%, and an F1 score of 95.83%. Compared to existing models, it exhibits significant advantages, offering robust technical support for the precise identification of maize diseases and pests.

Characterization and biocontrol potential of some rhizobacteria against fungal pathogens causing foliar diseases in maize

Maize is one of the most consumed cereal crops worldwide, and it is a strategic crop to the attainment of SDG 2 of Zero hunger. Despite its importance, the cultivation of maize has been significantly impaired by fungal pathogens causing foliar diseases. The occurrence of this disease in maize plantations at the Research Farm of the North-West University, Molelwane, Mafikeng, South Africa prompted this investigation. Samples of diseased maize rhizosphere soil were aseptically collected. Bacteria species associated with the rhizosphere were isolated and characterized as Bacillus siamensis, Enterobacter asburiae, Enterobacter chengduensis, Priestia aryabhattai, Burkholderia sp., Priestia megaterium strain AOA6 and Priestia megaterium strain AOA7. The anti-fungicidal potentials of the bacterial species were evaluated against pathogenic fungal species, Nigrospora sphaerica, Alternaria alternata and Fusarium equiseti in-vitro. The percentage mycelia growths were calculated and the data were subjected to ANOVA using SAS version 9.8. All the seven bacteria isolates tested positive to ammonia production, phosphate solubilization, siderophore production and ACC deaminase tests. The percentage mycelia inhibition showed Nigrospora sphaerica (36.29%), A. alternata (26.19%) and F. equiseti (20.63%) as the order of fungal inhibition by the bacteria species. Furthermore, E. asburiae > P. megatarium strain AOA7 > B. siamensis > P. aryabhattai > E. chengduensis > Bulkholderia sp. were the order of antifungal efficacy of the bacteria species evaluated. In conclusion, the efficacy of the bacteria especially E. asburiae, P. megatarium strain AOA7 and B. siamensis over various fungal pathogens. The result obtained, therefore, justifies the further investigation, formulation and deployment of the bacteria species as biofungicide in the management of foliar diseases of maize. Keywords: antifungicidal potential, biofungicide, microbial formulations, rhizosphere, zero hunger.

Breeding for Resistance to Biotic Stresses in Crops: Conventional to Genome Editing

Biotic stress is caused by fungi, bacteria, viruses, insects, nematodes as well weeds and it significantly reduces crop yield globally. For instance, the outbreak of a new strain of stripe rust in wheat worldwide, the emergence of white scale insect affecting mango in Ethiopia, the newly emerged Maize Lethal Necrosis viral disease in maize, and invasive fall armyworm insect are devastating maize crop and causing yield loss in Africa. To reduce the yield losses due to biotic stresses, the development of resistant variety and integrated insect pest approach is the way forward for managing disease and insects at respective agroecologies. Thus, this review paper discussed on conventional breeding methods and molecular-assisted selection for breeding resistance to foliar disease in major cereal crops. Wide array of germplasm such as landraces, recombinant inbred lines, pure lines, Double haploid lines, elite lines, multi-parent population, mutant lines, introgressed lines, hybrids, open population variety and wild relative can be used as source germplasm and should be screened under artificial inoculation and or at hotspot areas to develop disease resistance variety. Many maize inbred lines and hybrids showed resistance to turcicum leaf blight, grey leaf spot and common rust diseases and indicating that these genotypes have carrying genes/favorable alleles for multiple disease resistance and it is possible to develop variety resistance to fungi foliar disease in maize. Similarly, several advanced lines and some varieties showed resistance to strip and leaf rust in wheat. However, host plant resistance could be broken down due to new emerging race pathogens. Thus, conventional breeding and molecular screening should be integrated for resistant variety development. Indeed, Marker-assisted selection through backcrossing, gene pyramiding, combined Genome-Wide Association, and transcriptome approach is useful to identify candidate genes and resistant parents in crops. .......

Chitosan nanofertilizer to strengthen sink strength and provide resistance against PFSR (post flowering stalk rot) disease in maize

We herein report a significant increase in sink strength and disease resistance in maize crop using copper-salicylic acid co-encapsulated chitosan nanofertilizer in seed treatment and foliar application. These chitosan nanofertilizer (0.01–0.16%) exhibited significant antifungal activity against post-flowering stalk rot (PFSR) in pot house [45.2–73.5% (mycelia growth) and 40.0–79.6% (spore germination)]. In field conditions, chitosan nanofertilizer (0.01–0.16%) effectively controlled the PFSR disease with PEDC (percentage efficacy of disease control) values of 68.6–93.8%. The results are evident with the significant improvement in defence enzyme activities namely phenylalanine ammonia lyase (PAL, 4.9 fold increase) and polyphenol oxidase (PPO, 2.3 fold increase). Crop growth parameters namely plant height, ear height, cob weight, test weight, grain yield/plot and biomass were also significantly improved. The grain yield increased by 1.8 fold proving that up-regulated source activity is coupled with elevated sink strength. Overall, we claim that the developed chitosan nanofertilizer serves both as a fertilizer as well as a pesticide. The developed fertilizer has agricultural importance and could be tested for its potential use in other crops.

Genetic Response of Selected Maize Genotypes to Gray Leaf Spot (Cercospora zeina L.) Infestation

Gray leaf spot (GLS) caused by Cercospora zea-maydis and Cercospora zeina is one of the most yield limiting diseases of maize globally. Yield losses of up to 60 percent in susceptible genotypes are not uncommon. The objectives of the study were to evaluate the response of diverse maize genotypes to Gray leafspot infestation in western Kenya; determine the genetics of GLS resistance in maize inbred lines CML312, CML389 and to evaluate the relationship between GLS assessment methods, severity and lesion length. 13 maize inbred lines, 2F1 hybrids, and F2 populations of crosses MSN21 and CML389 or CML312 were evaluated under artificial GLS infestation during the 2007/08 seasons at Maseno university. Among the inbred lines, MSN21 was the most susceptible to GLS and had the highest disease severity rating. The inbred line CML389 and their F1 hybrids showed high levels of GLS resistance. CML312 and CML384 showed tolerance to GLS. Correlation between the lesion length and severity ratings was positive and highly significant (r = 0.9; P < 0.001), suggesting that both could be used in disease damage assessment. The frequency distribution of severity data for the F2 population of a cross between MSN21 and CML312 was continuous, suggesting that GLS tolerance is influenced by quantitative genes. A similar frequency distribution data for F2 population of a cross between MSN21 and CML389, showed 2 distinct peaks, and the genotypes within the 2 classes fitted a 9 to 7 ratio. This suggests that the resistance to GLS in CML389 may be conditioned by at least 2 major genes, with complementary epistatic interactions.

TinyML for smart agriculture: Comparative analysis of TinyML platforms and practical deployment for maize leaf disease identification

Maize, a vital global food crop, is susceptible to various diseases impacting agricultural yield and quality, necessitating effective monitoring strategies. This study presents a comprehensive approach utilizing advanced Convolutional Neural Network (CNN) architecture for efficient maize leaf disease detection and classification. Leveraging datasets from Kaggle, Harvard Dataverse, and Mendeley, the proposed CNN maize disease detection (MDD) system integrates optimization techniques, data augmentation, batch size adjustments, and fine-tuning strategies. Evaluation through data splitting reveals significant correlations among maize leaf classifications and the influence of data augmentation on image quality. Following training, the CNN-MDD model is deployed on a Microcontroller Unit (MCU) simulation, achieving promising optimization parameters: latency of 7.60 ms, RAM size of 726.60 KB, classifier flash size of 344.70 KB, and an accuracy of 94.60 %. These optimized parameters align well with MCU specifications, highlighting the potential for practical deployment on low-cost edge devices like Arduino Ble 33 Sense. Additionally, this research contributes to the creation of a Ghanaian maize crop pest and disease dataset and the development of an optimal and cost-effective CNN-MDD MCU edge device, aiming to provide local farmers in Ghana and Sub-Saharan Africa with a swift, offline solution for maize disease detection, thus fostering sustainable agricultural practices. We conduct a comparative analysis of Edge Impulse and TensorFlow, two prominent machine-learning platforms for TinyML model development and deployment in various applications, including smart crop disease detection. Utilizing a dataset comprising 12,344 raw images and 7,454 augmented images of healthy and diseased maize leaves, we evaluate the workflow, features, performance, and usability of each platform. Our findings indicate that Edge Impulse holds a slight advantage over TensorFlow in terms of ease of use, reliability, and memory footprint, whereas TensorFlow offers greater flexibility, customization options, and higher accuracy. We conclude that while both platforms are suitable for crop disease detection, a combined approach allows for leveraging TensorFlow's robust training capabilities for complex neural networks alongside the simplicity of Edge Impulse for deploying models on low-powered edge devices. This synergistic approach enhances the effectiveness and efficiency of crop disease detection systems, ultimately benefiting agricultural practices and food security.

Maize Leaf Disease Recognition Based on Improved Convolutional Neural Network ShuffleNetV2.

The occurrence of maize diseases is frequent but challenging to manage. Traditional identification methods have low accuracy and complex model structures with numerous parameters, making them difficult to implement on mobile devices. To address these challenges, this paper proposes a corn leaf disease recognition model SNMPF based on convolutional neural network ShuffleNetV2. In the down-sampling module of the ShuffleNet model, the max pooling layer replaces the deep convolutional layer to perform down-sampling. This improvement helps to extract key features from images, reduce the overfitting of the model, and improve the model's generalization ability. In addition, to enhance the model's ability to express features in complex backgrounds, the Sim AM attention mechanism was introduced. This mechanism enables the model to adaptively adjust focus and pay more attention to local discriminative features. The results on a maize disease image dataset demonstrate that the SNMPF model achieves a recognition accuracy of 98.40%, representing a 4.1 percentage point improvement over the original model, while its size is only 1.56 MB. Compared with existing convolutional neural network models such as EfficientNet, MobileViT, EfficientNetV2, RegNet, and DenseNet, this model offers higher accuracy and a more compact size. As a result, it can automatically detect and classify maize leaf diseases under natural field conditions, boasting high-precision recognition capabilities. Its accurate identification results provide scientific guidance for preventing corn leaf disease and promote the development of precision agriculture.

Antagonistic potential and biological control mechanisms of Pseudomonas strains against banded leaf and sheath blight disease of maize

Rhizoctonia solani, the causal agent of banded leaf and sheath blight (BL&SB), poses a significant threat to maize and various crops globally. The increasing concerns surrounding the environmental and health impacts of chemical fungicides have encouraged intensified concern in the development of biological control agents (BCAs) as eco-friendly alternatives. In this study, we explored the potential of 22 rhizobacteria strains (AS1–AS22) isolates, recovered from the grasslands of the Pithoragarh region in the Central Himalayas, as effective BCAs against BL&SB disease. Among these strains, two Pseudomonas isolates, AS19 and AS21, exhibited pronounced inhibition of fungal mycelium growth in vitro, with respective inhibition rates of 57.04% and 54.15% in cell cultures and 66.56% and 65.60% in cell-free culture filtrates. Additionally, both strains demonstrated effective suppression of sclerotium growth. The strains AS19 and AS21 were identified as Pseudomonas sp. by 16S rDNA phylogeny and deposited under accession numbers NAIMCC-B-02303 and NAIMCC-B-02304, respectively. Further investigations revealed the mechanisms of action of AS19 and AS21, demonstrating their ability to induce systemic resistance (ISR) and exhibit broad-spectrum antifungal activity against Alternaria triticina, Bipolaris sorokiniana, Rhizoctonia maydis, and Fusarium oxysporum f. sp. lentis. Pot trials demonstrated significant reductions in BL&SB disease incidence (DI) following foliar applications of AS19 and AS21, with reductions ranging from 25 to 38.33% compared to control treatments. Scanning electron microscopy revealed substantial degradation of fungal mycelium by the strains, accompanied by the production of hydrolytic enzymes. These findings suggest the potential of Pseudomonas strains AS19 and AS21 as promising BCAs against BL&SB and other fungal pathogens. However, further field trials are warranted to validate their efficacy under natural conditions and elucidate the specific bacterial metabolites responsible for inducing systemic resistance. This study contributes to the advancement of sustainable disease management strategies and emphasizes the potential of Pseudomonas strains AS19 and AS21 in combating BL&SB and other fungal diseases affecting agricultural crops.

Draft genome sequences of two biocontrol agents isolated from the maize phyllosphere: Bacillus subtilis strain EM-A7 and Bacillus velezensis strain EM-A8

In the present study, the genomes of B. subtilis EM-A7 and B. velezensis EM-A8 were sequenced and annotated. The Illumina sequencing platform (NovaSeq PE150) was used to sequence the genomic DNA. There were 6 277 054 raw reads for EM-A7, with a Q20 of 97.52 % and 43.78 % GC, and 8 030 262 raw reads for EM-A8, with a Q20 of 97.53 % and 46.21 % GC. Annotation was carried out by the NCBI Prokaryotic Genome Annotation Pipeline (PGAP). The strains were classified taxonomically on the basis of an average nucleotide identity analysis (ANI), as well as through a dDDh analysis on the Genome-to-Genome Distance Calculator (GGDC v3.0). The pipeline predicted 4062 protein-coding sequences (CDSs) and 73 RNA genes (62 tRNA and 6 rRNA) for EM-A7, and 3797 protein-coding sequences (CDSs) and 80 RNA genes for EM-A8. These findings enhance our understanding of the two strains’ potential as biocontrol agents to manage disease in maize.

Etiology, management, and host response of cold-tolerant maize germplasm for common rust disease of maize under temperate conditions

Etiology, management, and host response of cold-tolerant maize germplasm for common rust disease of maize under temperate conditions

Maize leaf disease recognition based on improved MSRCR and OSCRNet

Accurate identification of maize pests and diseases plays a crucial role in the growth and yield of maize. The recognition method of maize leaf diseases based on deep learning is currently a research hotspot. In practical applications, it is difficult to ensure a high recognition accuracy due to the impact of environmental factors (such as light intensity, background noise) and insufficient training data. To address this challenge, this paper proposes a maize image recognition method based on image enhancement and OSCRNet. Firstly, a maize leaf image enhancement framework and algorithm based on improved MSRCR were designed to enhance the characteristics of the maize leaf in a complex environment and to solve the problems of high noise and blur of maize images. Subsequently, the OSCRNet maize leaf recognition network model was established, which uses octave convolution with characteristics to accelerate network training and reduce unnecessary redundant spatial information in maize leaf images. Additionally, a self-calibrated convolution with multi-scale features was employed to realize the interactions of different feature information in the maize leaf images, enhance feature extraction, and solve the problems of similarity of maize disease features and easy learning disorders. Concurrently, batch normalization was employed to prevent network overfitting and enhance the robustness of the model. The experimental results showed that the highest identification accuracy of the proposed method for rust, gray leaf disease, northern wilt disease, and healthy maize are 96.67%, 94.86%, 92.81%, and 95.63%, respectively. Our methods were beneficial in solving the problems of slow efficiency, low accuracy and insufficient training data, and also outperformed other comparison models. The proposed method demonstrates strong robustness for maize disease images collected in the natural environment, providing a reference for the intelligent diagnosis of other plant leaf diseases.

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.