Journal articles: 'Maize diseases'

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Subedi, Subash. "A review on important maize diseases and their management in Nepal." Journal of Maize Research and Development 1, no. 1 (December 30, 2015): 28–52. http://dx.doi.org/10.3126/jmrd.v1i1.14242.

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In Nepal, maize ranks second after rice both in area and production. In recent years, maize area and production has shown a steady increase, but productivity has been low (2.46 t/ha). The major maize producing regions in Nepal are mid hill (72.85%), terai (17.36%) and high hill (9.79%) respectively. A literature review was carried out to explore major maize diseases and their management in Nepal. The omnipresent incidence of diseases at the pre harvest stage has been an important bottleneck in increasing production. Till now, a total of 78 (75 fungal and 3 bacterial) species are pathogenic to maize crop in Nepal. The major and economically important maize diseases reported are Gray leaf spot, Northern leaf blight, Southern leaf Blight, Banded leaf and sheath blight, Ear rot, Stalk rot, Head smut, Common rust, Downy mildew and Brown spot. Information on bacterial and virus diseases, nematodes and yield loss assessment is also given. Description of the major maize diseases, their causal organisms, distribution, time and intensity of disease incidence, symptoms, survival, spreads, environmental factors for disease development, yield losses and various disease management strategies corresponded to important maize diseases of Nepal are gathered and compiled thoroughly from the available publications. Concerted efforts of NARC commodity programs, divisions, ARS and RARS involving research on maize pathology and their important outcomes are mentioned. The use of disease management methods focused on host resistance has also been highlighted.Journal of Maize Research and Development (2015) 1(1):28-52DOI: http://dx.doi.org/10.5281/zenodo.34292

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Rehman, Fazal Ur, Muhammad Adnan, Maria Kalsoom, Nageen Naz, Muhammad Ghayoor Husnain, Haroon Ilahi, Muhammad Asif Ilyas, Gulfam Yousaf, Rohoma Tahir, and Usama Ahmad. "Seed-Borne Fungal Diseases of Maize (Zea mays L.): A Review." Agrinula : Jurnal Agroteknologi dan Perkebunan 4, no. 1 (February 12, 2021): 43–60. http://dx.doi.org/10.36490/agri.v4i1.123.

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Introduction: Maize (Zea mays) is one of the most important cereal crops. It is ranked as 3rd after wheat and rice. Due to its wide adaptability, diversified uses, and low production costs, it has great potential as a cereal crop. In the case of yield losses, various factors are involved. The fungal diseases of maize play a significant role in the reduction of both quantity as well as the quality of maize. Review Results: At the seedling stage, maize suffers from numerous diseases and many of them are seed-borne diseases. Anthracnose stalk rot (Colletotrichum graminicola), Charcoal rot of maize (Macrophomina phaseolina), Crazy top downy mildew disease (Sclerophthora macrospora), Corn grey leaf spot disease (Cercospora zeae-maydis), Aspergillus ear and kernel rot (Aspergillus flavus), Corn smut (Ustilago maydis), Southern corn leaf blight disease (Bipolaris maydis) etc. are important among these diseases.Chemical control of seed-borne pathogens of maize is rather difficult to achieve as a reasonably good. Due to the hazardous environmental effects of chemicals, the Integrated Management of the seed-borne fungal pathogens of corn is mostly preferred. The distribution, disease cycle, symptoms of the damage, effects of environmental factors, economical importance of disease, and integrated disease management options of major seed-borne fungal pathogens of maize have been reviewed in this review article from various currently available sources.

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Frommer, Dóra, Szilvia Veres, and László Radócz. "Sensitivity of maize hybrids to common smut under field artificial inoculation conditions." Acta Agraria Debreceniensis, no. 71 (June 14, 2017): 25–28. http://dx.doi.org/10.34101/actaagrar/71/1566.

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Common smut disease of maize is one of the most frequent diseases of crop. In the last decades the importance of disease has decreased in feeding maize production, however its importance increasing again nowadays, especially at sweet maize hybrids. The aims of this work was to find hybrids possess of resistance, and to evaluate which ones are more or less susceptible under field artificial inoculation circumstances. Among feeding maizes the less susceptible hybrid was ‘P9578’, and the most susceptible ’NK Columbia’ hybrid, and differences in cob infection between them was significant (8.8%). At sweet corn hybrids the less susceptible was ’Prelude’, while the most susceptible was ’Jumbo’ with very high significant 74.6% differences.

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Batchelor, William D., L. M. Suresh, Xiaoxing Zhen, Yoseph Beyene, Mwaura Wilson, Gideon Kruseman, and Boddupalli Prasanna. "Simulation of Maize Lethal Necrosis (MLN) Damage Using the CERES-Maize Model." Agronomy 10, no. 5 (May 15, 2020): 710. http://dx.doi.org/10.3390/agronomy10050710.

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Maize lethal necrosis (MLN), maize streak virus (MSV), grey leaf spot (GLS) and turcicum leaf blight (TLB) are among the major diseases affecting maize grain yields in sub-Saharan Africa. Crop models allow researchers to estimate the impact of pest damage on yield under different management and environments. The CERES-Maize model distributed with DSSAT v4.7 has the capability to simulate the impact of major diseases on maize crop growth and yield. The purpose of this study was to develop and test a method to simulate the impact of MLN on maize growth and yield. A field experiment consisting of 17 maize hybrids with different levels of MLN tolerance was planted under MLN virus-inoculated and non-inoculated conditions in 2016 and 2018 at the MLN Screening Facility in Naivasha, Kenya. Time series disease progress scores were recorded and translated into daily damage, including leaf necrosis and death, as inputs in the crop model. The model genetic coefficients were calibrated for each hybrid using the 2016 non-inoculated treatment and evaluated using the 2016 and 2018 inoculated treatments. Overall, the model performed well in simulating the impact of MLN damage on maize grain yield. The model gave an R2 of 0.97 for simulated vs. observed yield for the calibration dataset and an R2 of 0.92 for the evaluation dataset. The simulation techniques developed in this study can be potentially used for other major diseases of maize. The key to simulating other diseases is to develop the appropriate relationship between disease severity scores, percent leaf chlorosis and dead leaf area.

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Kaefer, Kaian Albino Corazza, Adilson Ricken Schuelter, Ivan Schuster, Jonatas Marcolin, and Eliane Cristina Gruszka Vendruscolo. "Identification and characterization of maize lines resistant to leaf diseases." Semina: Ciências Agrárias 40, no. 2 (April 15, 2019): 517. http://dx.doi.org/10.5433/1679-0359.2019v40n2p517.

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Among the maize leaf diseases, white leaf spot, northern leaf blight, gray leaf spot, and southern rust are recognized not only by the potential for grain yield reduction but also by the widespread occurrence in the producing regions of Brazil and the world. The aim of this study was to characterize common maize lines for resistance to white leaf spot, northern leaf blight, gray leaf spot, and southern rust and suggest crosses based on the genetic diversity detected in SNP markers. The experiment was conducted in a randomized block design with three replications in order to characterize 72 maize lines. Genotypic values were predicted using the REML/BLUP procedure. These 72 lines were genotyped with SNP markers using the 650K platform (Affymetrix®) for the assessment of the genetic diversity. Genetic diversity was quantified using the Tocher and UPGMA methods. The existence of genetic variability for disease resistance was detected among maize lines, which made possible to classify them into three large groups (I, II, and III). The maize lines CD 49 and CD50 showed a good performance and can be considered sources of resistance to diseases. Therefore, their use as gene donors in maize breeding programs is recommended. Considering the information of genetic distance together with high heritability for leaf diseases, backcrossing of parent genotypes with different resistance levels, such as those of the lines CD49 x CD69 and CD50 x CD16, may result in new gene combinations, as they are divergent and meet good performances.

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Wei, Yuchen, Lisheng Wei, Tao Ji, and Huosheng Hu. "A Novel Image Classification Approach for Maize Diseases Recognition." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 3 (May 18, 2020): 331–39. http://dx.doi.org/10.2174/2352096511666181003134208.

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Background: The spot, streak and rust are the most common diseases in maize, all of which require effective methods to recognize, diagnose and handle. This paper presents a novel image classification approach to the high accuracy recognition of these maize diseases. Methods: Firstly, the k-means clustering algorithm is deployed in LAB color space to reduce the influence of image noise and irrelevant background, so that the area of maize diseases could be effectively extracted. Then the statistic pattern recognition method and gray level co-occurrence matrix (GLCM) method are jointly used to segment the maize disease leaf images for accurately obtaining their texture, shape and color features. Finally, Support Vector Machine (SVM) classification method is used to identify three diseases. Results: Numerical results clearly demonstrate the feasibility and effectiveness of the proposed method. Conclusion: Our future work will focus on the investigation of how to use the new classification methods in dimensional and large scale data to improve the recognizing performance and how to use other supervised feature selection methods to improve the accuracy further.

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Dodd, James, and Denis C. McGee. "Maize Diseases: A Reference Source for Seed Technologists." Mycologia 81, no. 3 (May 1989): 493. http://dx.doi.org/10.2307/3760093.

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Broadhurst, P. G. "Diseases of maize in Waikato and South Auckland." Proceedings of the New Zealand Plant Protection Conference 51 (August 1, 1998): 260. http://dx.doi.org/10.30843/nzpp.1998.51.11686.

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Charles, Alice K., William M. Muiru, Douglas W. Miano, and John W. Kimenju. "Distribution of Common Maize Diseases and Molecular Characterization of Maize Streak Virus in Kenya." Journal of Agricultural Science 11, no. 4 (March 15, 2019): 47. http://dx.doi.org/10.5539/jas.v11n4p47.

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Maize is an important food crop in Kenya and is susceptible to a wide range of diseases. A survey was conducted in 2012 in different agro-ecological zones (AEZ) of Kiambu, Embu and Nakuru counties to determine the distribution of northern leaf blight (NLB), common rust (CR), maize streak disease (MSD), gray leaf spot (GLS), head smut (HS) and common smut (CS). Data collected included prevalence, incidence and severity of each of the diseases. Maize leaf samples infected with MSD were also collected for molecular characterization of Maize streak virus (MSV). Northern leaf blight was reported in all counties surveyed with 100% disease prevalence. Kiambu had the highest incidence (100%) of CR whereas Embu had the highest prevalence (45%) of MSD. The incidences of GLS and HS were very low with averages of below 2.5%. The highest incidence of GLS was in Kiambu (5%). High altitude areas had higher incidences of NLB and GLS while CS and MSD were widespread in the three counties. Comparison of 797 nucleotides from the open reading frame (ORF) C2/C1 of MSV with other sequences from the GenBank showed sequence similarities of 99 to 100% with MSV-A strain. The study revealed that the major foliar diseases of maize are widespread in Kenya and therefore there is need to institute measures to manage these diseases and reduce associated losses. Also, the high percent sequence similarities of MSV indicate low variability which is good for breeders since developed resistant varieties can be adopted over a wider region.

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Luo, Jing, Shuze Geng, Chunbo Xiu, Dan Song, and Tingting Dong. "A Curvelet-SC Recognition Method for Maize Disease." Journal of Electrical and Computer Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/164547.

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Because the corn vein and noise influence the contour extraction of the maize leaf disease, we put forward a new recognition algorithm based on Curvelet and Shape Context (SC). This method can improve the speed and accuracy of maize leaf disease recognition. Firstly, we use Seeded Regional Growing (SRG) algorithm to segment the maize leaf disease image. Secondly, Curvelet Modulus Correlation (CMC) method is put forward to extract the effective contour of maize leaf disease. Thirdly, we combine CMC with the SC algorithm to obtain the histogram features and then use these features we obtain to calculate the similarities between the template image and the target image. Finally, we adoptn-fold cross-validation algorithm to recognize diseases on maize leaf disease database. Experimental results show that the proposed algorithm can recognize 6 kinds of maize leaf diseases accurately and achieve the accuracy of 94.446%. Meanwhile this algorithm has guiding significance for other diseases recognition to an extent.

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Olajide, Olajide Blessing, Odeniyi Olufemi Ayodeji, Olabiyi Olatunji Coker, Adewale Joseph Adekunle, and Yakubani Yakubu. "A Fuzzy Inference System for Maize Plant Yield Prediction." International Journal of Innovative Technology and Exploring Engineering 10, no. 11 (September 30, 2021): 90–96. http://dx.doi.org/10.35940/ijitee.k9493.09101121.

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Ascertaining infections in maize plants is through observation of the crop plant for visual indications which a farmer is able to relate to specific diseases. The perception of the farmer is prone to human error which may sometimes link some symptoms to the wrong disease and could impact the application of suitable preventive and curable routines to combat the identified diseases. Hence, accurate identification of crop plant disease is of high importance to a farmer to aid response to diseases. The objective of this article is to apply fuzzy set and interpolation technique to develop an expert system to carry out field-based identification and yield forecast for the maize plant. For this study, some associated factors were recognized for maize plant diseases and confirmed by a professional Botanists. For this study, a number of associated factors were identified for maize plant diseases and validated by experienced Botanists. Further to this, triangular membership functions was used to develop the fuzzy inference system model following the preprocessing of identified factors and related output. 32 inferred rules were formulated using IF-THEN statements which adopted the values of the factors as antecedent and the yield of maize plant as the consequent part of each rule for classification of the yield of maize plant. The Fuzzy model was simulated for each of the identified five factors. The simulation results showed that the risk factors identified; black moldy growth on kernels and ears, blights on leaves, rotten cobs, infected husks and black kernels and seed decay have noticeable influence on the maize plant yield if timely remedy is not administered. The study established that the utilisation of fuzzy technique is helpful to appraise the yield of maize such that the lesser the manifestation of identified associated features then the higher the yield of the maize plant.

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Poudel, Nabin Sharma, and Kapil Khanal. "Viral Diseases of Crops in Nepal." International Journal of Applied Sciences and Biotechnology 6, no. 2 (June 29, 2018): 75–80. http://dx.doi.org/10.3126/ijasbt.v6i2.19702.

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Viral diseases are the important diseases next to the fungal and bacterial in Nepal. The increase in incidence and severity of viral diseases and emergence of new viral diseases causes the significant yield losses of different crops in Nepal. But the research and studies on plant viral diseases are limited. Most of the studies were focused in viral diseases of rice (Rice tungro virus and Rice dwarf virus), tomato (Yellow leaf curl virus) and potato (PVX and PVY). Maize leaf fleck virus and mosaic caused by Maize mosaic virus were recorded as minor disease of maize. Citrus Tristeza Virus is an important virus of citrus fruit in Nepal while Papaya ringspot potyvirus, Ageratum yellow vein virus (AYVV), Tomato leaf curlJava betasatellite and Sida yellow vein Chinaalphasatellite were recorded from the papaya fruit. The Cucumber mosaic virus (CMV) and Zucchini yellow mosaic potyvirus (ZYMV) are the viral diseases of cucurbitaceous crop reported in Nepal. Mungbean yellow mosaic India virus (MYMIV) found to infect the many crops Limabean, Kidney bean, blackgram and Mungbean. Bean common mosaic necrosis virus in sweet bean, Pea leaf distortion virus (PLDV), Cowpea aphid‐borne mosaic potyvirus (CABMV), Peanut bud necrosis virus (PBNV) in groundnut, Cucumber mosaic virus (CMV). Chili veinal mottle potyvirus (CVMV) and Tomatoyellow leaf curl gemini virus (TYLCV) were only reported and no any further works have been carried out. The 3 virus diseases Soyabean mosaic (SMV), Soybean yellow mosaic virus and Bud blight tobacco ring spot virus (TRSV) were found in soybean.Int. J. Appl. Sci. Biotechnol. Vol 6(2): 75-80

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Yahaya, Adama, Danladi B. Dangora, Olufemi J. Alabi, Aisha M. Zongoma, and P. Lava Kumar. "Detection and diversity of maize yellow mosaic virus infecting maize in Nigeria." VirusDisease 30, no. 4 (November 21, 2019): 538–44. http://dx.doi.org/10.1007/s13337-019-00555-0.

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Kannan, Maathavi, Ismanizan Ismail, and Hamidun Bunawan. "Maize Dwarf Mosaic Virus: From Genome to Disease Management." Viruses 10, no. 9 (September 13, 2018): 492. http://dx.doi.org/10.3390/v10090492.

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Maize dwarf mosaic virus (MDMV) is a serious maize pathogen, epidemic worldwide, and one of the most common virus diseases for monocotyledonous plants, causing up to 70% loss in corn yield globally since 1960. MDMV belongs to the genus Potyvirus (Potyviridae) and was first identified in 1964 in Illinois in corn and Johnsongrass. MDMV is a single stranded positive sense RNA virus and is transmitted in a non-persistent manner by several aphid species. MDMV is amongst the most important virus diseases in maize worldwide. This review will discuss its genome, transmission, symptomatology, diagnosis and management. Particular emphasis will be given to the current state of knowledge on the diagnosis and control of MDMV, due to its importance in reducing the impact of maize dwarf mosaic disease, to produce an enhanced quality and quantity of maize.

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Brewbaker, James L. "Registration of Nine Maize Populations Resistant to Tropical Diseases." Journal of Plant Registrations 3, no. 1 (January 2009): 10–13. http://dx.doi.org/10.3198/jpr2008.07.0396crc.

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Payak, M. M., and R. C. Sharma. "Maize diseases and approaches to their management in India." Tropical Pest Management 31, no. 4 (January 1985): 302–10. http://dx.doi.org/10.1080/09670878509371006.

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THOTTAPPILLY, G., N. A. BOSQUE-PÉREZ, and H. W. ROSSEL. "Viruses and virus diseases of maize in tropical Africa." Plant Pathology 42, no. 4 (August 1993): 494–509. http://dx.doi.org/10.1111/j.1365-3059.1993.tb01529.x.

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Li, Mingjun, Xi Sun, Dianping Di, Aihong Zhang, Ling Qing, Tao Zhou, Hongqin Miao, and Zaifeng Fan. "Maize AKINβγ Proteins Interact with P8 of Rice Black Streaked Dwarf Virus and Inhibit Viral Infection." Viruses 12, no. 12 (December 4, 2020): 1387. http://dx.doi.org/10.3390/v12121387.

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Rice black streaked dwarf virus (RBSDV) is an important agent causing maize rough dwarf disease, whereas the host factors responding to RBSDV infection are poorly understood. To uncover the molecular interactions between RBSDV and maize, a yeast two-hybrid screen of a maize cDNA library was carried out using the viral P8 protein as a bait. ZmAKINβγ-1 and ZmAKINβγ-2 (βγ subunit of Arabidopsis SNF1 kinase homolog in maize) possessing high sequence similarities (encoded by two gene copies) were identified as interaction partners. Their interactions with P8 were confirmed in both Nicotiana benthamiana cells and maize protoplasts by bimolecular fluorescence complementation assay. The accumulation levels of ZmAKINβγ mRNAs were upregulated at the stage of the viral symptoms beginning to appear and then downregulated. ZmAKINβγs are putative regulatory subunits of the SnRK1 complex, a core regulator for energy homeostasis. Knockdown of ZmAKINβγs in maize regulated the expression levels of the genes involved in sugar synthesis or degradation, and also the contents of both glucose and sucrose. Importantly, downregulation of ZmAKINβγs expressions facilitated the accumulation of RBSDV in maize. These results implicate a role of ZmAKINβγs in the regulation of primary carbohydrate metabolism, and in the defense against RBSDV infection.

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Munkvold, G. P., and R. L. Hellmich. "Genetically Modified, Insect Resistant Maize: Implications for Management of Ear and Stalk Diseases." Plant Health Progress 1, no. 1 (January 2000): 17. http://dx.doi.org/10.1094/php-2000-0912-01-rv.

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Because the fungi that produce mycotoxins in maize are frequently associated with insect damage, insect control has the potential to reduce mycotoxin concentrations in grain. Our research indicates that Bt transformation of maize hybrids enhances the safety of grain for livestock and human food products by reducing the plants' vulnerability to mycotoxin-producing Fusarium fungi. Accepted for publication 5 September 2000. Published 12 September 2000.

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Hao, Jun Jie, Shu Na Xie, Jing Sun, Gong Qiang Yang, Jia Zhong Liu, Fei Xu, Yan Yan Ru, and Yu Li Song. "Analysis of Fusarium graminearum Species Complex from Wheat–Maize Rotation Regions in Henan (China)." Plant Disease 101, no. 5 (May 2017): 720–25. http://dx.doi.org/10.1094/pdis-06-16-0912-re.

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Fusarium head blight (FHB) and maize stalk rot (MSR), caused by members of the Fusarium graminearum species complex (FGSC), are among the most destructive and economically important diseases in the world. Species identity and the trichothecene chemotype of 312 members of the FGSC from diseased wheat spikes and maize stalks in Henan was determined using phylogenetic analyses and a polymerase chain reaction trichothecene chemotype assay. F. graminearum sensu stricto accounted for more than 93% of the FGSC isolates associated with FHB (N = 168) and MSR (N = 130). The remaining isolates were F. asiaticum. Significant differences were found in the frequencies of the two species within the hosts (P < 0.01). However, the frequencies of the same species in FHB and MSR were similar (P > 0.05) for wheat and maize isolates, indicating that the composition of the FGSC with respect to wheat and maize in these fields varied little. The 15-acetyl-deoxynivalenol (15-ADON) trichothecene chemotype represented 92.7 and 98.5% of isolates from wheat (N = 167) and maize (N = 130), respectively. However, the 3-acetyl-deoxynivalenol chemotype was found in 6.7% of wheat isolates, and the nivalenol chemotype in 1.5% of MSR isolates and in 0.6% of FHB isolates. Mycelial growth at different concentrations of carbendazim and difenoconazole did not differ between F. graminearum sensu stricto and F. asiaticum. These results suggest that the 15-ADON chemotype of F. graminearum sensu stricto is the predominant pathogen that causes wheat- and maize-related diseases in this region. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

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Yahaya, Adama, Danladi B. Dangora, Olufemi J. Alabi, Aisha M. Zongoma, and P. Lava Kumar. "Correction To: Detection and diversity of maize yellow mosaic virus infecting maize in Nigeria." VirusDisease 31, no. 3 (April 5, 2020): 396–97. http://dx.doi.org/10.1007/s13337-020-00576-0.

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Hebbar, K. P., A. G. Davey, J. Merrin, T. J. McLoughlin, and P. J. Dart. "Pseudomonas cepacia, a potential suppressor of maize soil-borne diseases—Seed inoculation and maize root colonization." Soil Biology and Biochemistry 24, no. 10 (October 1992): 999–1007. http://dx.doi.org/10.1016/0038-0717(92)90028-v.

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Cao, Ning, Binhui Zhan, and Xueping Zhou. "Nitric Oxide as a Downstream Signaling Molecule in Brassinosteroid-Mediated Virus Susceptibility to Maize Chlorotic Mottle Virus in Maize." Viruses 11, no. 4 (April 22, 2019): 368. http://dx.doi.org/10.3390/v11040368.

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Maize chlorotic mottle virus (MCMV) infection causes growth abnormalities in maize. Transcriptome sequencing was conducted to compare the global gene expression of MCMV-inoculated plants with that of mock-inoculated plants. Data analyses showed that brassinosteroid (BR)-associated genes were upregulated after MCMV infection. Exogenous 2,4-epibrassinolide (BL) or brassinazole (BRZ) applications indicated that BR pathway was involved in the susceptibility to MCMV infection. In addition, treatment of BL on maize induced the accumulation of nitric oxide (NO), and the changes of NO content played positive roles in the disease incidence of MCMV. Moreover, MCMV infection was delayed when the BL-treated plants were applied with NO scavenger, which suggested that BR induced the susceptibility of maize to MCMV infection in a NO-dependent manner. Further investigation showed the maize plants with knock-down of DWARF4 (ZmDWF4, a key gene of BR synthesis) and nitrate reductase (ZmNR, a key gene of NO synthesis) by virus-induced gene silencing displayed higher resistance to MCMV than control plants. Taken together, our results suggest that BR pathway promotes the susceptibility of maize to MCMV in a NO-dependent manner.

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Ilbağı, Havva, Frank Rabenstein, Antje Habekuss, Frank Ordon, and Ahmet Çıtır. "Incidence of virus diseases in maize fields in the Trakya region of Turkey." Phytoprotection 87, no. 3 (May 29, 2007): 115–22. http://dx.doi.org/10.7202/015853ar.

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Abstract A survey on maize virus diseases was conducted in the Trakya region of Turkey by examining 32 496 and 46 871 plants in 2004 and 2005, respectively. Rates of symptomatic plants were estimated at 3.7 to 63.6%, depending on locations. Biological and serological test results revealed the presence of barley yellow dwarf virus-PAV (BYDV-PAV), maize dwarf mosaic virus (MDMV), sugarcane mosaic virus (SCMV), and Johnson grass mosaic virus (JGMV). One hundred forty-two samples were collected randomly from 6492 symptomatic plants in 2004. Seventy-two out of the 142 samples were infected with MDMV, two were infected with BYDV-PAV, 19 with MDMV and BYDV-PAV, two with MDMV, BYDV-PAV and SCMV, and only one sample contained the four viruses. In 2005, 100 other leaf samples were collected randomly from 11 739 symptomatic maize plants. Serological tests revealed that 50% of the samples were infected with MDMV and SCMV; however, five showed mixed infections of two or three combinations of tested viruses. Individual MDMV, SCMV, BYDV-PAV and JGMV infections were detected in five, three, two and four samples, respectively. Presence of MDMV was confirmed by Western blot analysis and IC-RT-PCR. SCMV was also detected by IC-RT-PCR. This is the first study reporting the detection of SCMV and JGMV on maize plants in Turkey.

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Sibiya, Malusi, and Mbuyu Sumbwanyambe. "A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks." AgriEngineering 1, no. 1 (March 13, 2019): 119–31. http://dx.doi.org/10.3390/agriengineering1010009.

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Plant leaf diseases can affect plant leaves to a certain extent that the plants can collapse and die completely. These diseases may drastically decrease the supply of vegetables and fruits to the market, and result in a low agricultural economy. In the literature, different laboratory methods of plant leaf disease detection have been used. These methods were time consuming and could not cover large areas for the detection of leaf diseases. This study infiltrates through the facilitated principles of the convolutional neural network (CNN) in order to model a network for image recognition and classification of these diseases. Neuroph was used to perform the training of a CNN network that recognised and classified images of the maize leaf diseases that were collected by use of a smart phone camera. A novel way of training and methodology was used to expedite a quick and easy implementation of the system in practice. The developed model was able to recognise three different types of maize leaf diseases out of healthy leaves. The northern corn leaf blight (Exserohilum), common rust (Puccinia sorghi) and gray leaf spot (Cercospora) diseases were chosen for this study as they affect most parts of Southern Africa’s maize fields.

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Assefa, Sintayehu, Tadesse Daniel, and Gebremedhin Zenebe. "Preliminary survey of foliar maize diseases in North Western Ethiopia." African Journal of Agricultural Research 13, no. 45 (November 8, 2018): 2591–601. http://dx.doi.org/10.5897/ajar2018.13517.

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Santiago, Rogelio, and Rosa Malvar. "Role of Dehydrodiferulates in Maize Resistance to Pests and Diseases." International Journal of Molecular Sciences 11, no. 2 (February 9, 2010): 691–703. http://dx.doi.org/10.3390/ijms11020691.

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Plotnytska, N., O. Nevmerzhytska, O. Gurmanchuk, and V. Kashtan. "THE DISINFECTANS EFFECTIVENESS APPLIED FOR MAIZE PROTECTION AGAINST FUNGI DISEASES." Scientific horizons 87, no. 2 (2020): 32–37. http://dx.doi.org/10.33249/2663-2144-2020-87-02-32-37.

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Liu, Jiangchuan, Mantao Wang, Lie Bao, and Xiaofan Li. "EfficientNet based recognition of maize diseases by leaf image classification." Journal of Physics: Conference Series 1693 (December 2020): 012148. http://dx.doi.org/10.1088/1742-6596/1693/1/012148.

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Xu, Jing, Teng Miao, Yuncheng Zhou, Yang Xiao, Hanbing Deng, Ping Song, and Kai Song. "Classification of maize leaf diseases based on hyperspectral imaging technology." Journal of Optical Technology 87, no. 4 (April 1, 2020): 212. http://dx.doi.org/10.1364/jot.87.000212.

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Alehegn, Enquhone. "Ethiopian maize diseases recognition and classification using support vector machine." International Journal of Computational Vision and Robotics 9, no. 1 (2019): 90. http://dx.doi.org/10.1504/ijcvr.2019.098012.

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Alehegn, Enquhone. "Ethiopian Maize Diseases Recognition and Classification using: Support Vector Machine." International Journal of Computational Vision and Robotics 9, no. 3 (2019): 1. http://dx.doi.org/10.1504/ijcvr.2019.10017481.

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Wu, Yuhao. "Identification of Maize Leaf Diseases based on Convolutional Neural Network." Journal of Physics: Conference Series 1748 (January 2021): 032004. http://dx.doi.org/10.1088/1742-6596/1748/3/032004.

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Nagy, E., V. Haş, I. Haş, A. Suciu, and V. Florian. "The influence of Fusarium ear infection on the maize yield and mycotoxin content (Transylvania-Romania)." Plant Breeding and Seed Science 64, no. 1 (January 1, 2011): 35–44. http://dx.doi.org/10.2478/v10129-011-0026-x.

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The influence of Fusarium ear infection on the maize yield and mycotoxin content (Transylvania-Romania) Maize is the host for a large number of pathogens, which invade all of its organs from the germination until being harvest, ear and grain infection often persistent even during storage. Diseases, through their symptoms reduce significantly the quantity and the quality of yield, estimated between 7-17% but, in the favorable years for this disease, they can be much greater. Fusarium diseases reduce yield value and quality by massive accumulation of Fusarium mycelium biomass (about 85%) on grain and ears and by mycotoxin contamination such as deoxynivalenol (DON), zearalenone (ZEA) and fumonisins (FUM). In this paper are presented aspects regarding the reaction of some maize hybrids under Fusarium spp. natural and artificial infections; the effect of Fusarium ear infection on yield, grain chemical composition, and mycotoxin content; the correlation between ear rot disease degree and yield ability, starch, protein and fat content. ANOVA evidenced the significant influence of experimental factors: infection conditions with Fusarium spp., maize genotypes, and their interaction on expression of the disease degree, yield capacity, protein, starch, fat and DON content. Average yield losses ranged between 7,0-9,3% during the experimental period. The hybrids Turda Star and Turda Favorit were more resistant to Fusarium ear rot, and Turda 165 was the most susceptible one. The artificial infection of ear with Fusarium spp. determined significantly decrease of starch and fat content and increases the protein and DON content for the most part of maize hybrids. Between rot diseased kernels and DON content a positive correlation was determined.

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Xia, Zheng Wei, and Fu Juan Wang. "The Application of Multifractal Theory and LVQ Neural Network in Maize Disease Intelligent Identification." Advanced Materials Research 671-674 (March 2013): 3165–69. http://dx.doi.org/10.4028/www.scientific.net/amr.671-674.3165.

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In this paper, according to the different maize disease images having different shape features，eight multi-fractal spectrum values were extracted as shape characteristic parameters of maize diseases, and then they were used to index on image data base. We applied learning vector quantization（LVQ）neural network to simple training，classfication and recognition. The method for recognition of maize disease can reach a higher recognition rate.

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Pfeiffer, Tobias, Astrid von Galen, Petra Zink, Sebastian Hübner, Ada Linkies, Dieter Felgentreu, Jannika Drechsel, et al. "Selection of bacteria and fungi for control of soilborne seedling diseases of maize." Journal of Plant Diseases and Protection 128, no. 5 (August 3, 2021): 1227–41. http://dx.doi.org/10.1007/s41348-021-00498-z.

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AbstractPlant-based screening experiments were conducted with the aim of identifying biocontrol bacteria and fungi for seed treatment of maize. Candidate microorganisms were evaluated for their protective effects against soilborne infections by species of Fusarium, Globisporangium (syn. Pythium) and Rhizoctonia. The microorganisms tested were bacteria and fungi from maize roots or other sources, including some active microbial components of commercial biocontrol products. Due to the method of isolation chosen, the majority of bacteria from maize roots were spore formers, most of them species of the genera Bacillus,Brevibacillus and Paenibacillus. In pot tests with the potting substrate inoculated with F. culmorum, the level of control provided by seed treatment with the most efficacious bacterial and fungal isolates was comparable or close to the chemical reference seed treatment thiram. The most effective bacteria were species of Pseudomonas, Burkholderia and Streptomyces. Among a subset of approx. 100 bacteria studied, the in vivo and in vitro activities against F.culmorum were only weakly correlated, although some strains deviated from this pattern. The most effective fungi were two strains of Clonostachys rosea and isolates of Trichoderma. The latter and a strain of Gliocladium virens provided also protection against R.solani. Activity against Globisporangium ultimum was recorded for one isolate of Trichoderma and the two strains of C. rosea. A reduction in the impact of seedborne F. culmorum was also observed after seed treatment with two strains of F. oxysporum f. sp. strigae. The results are discussed in relation to previous reports on rhizosphere bacteria of maize and their use in biocontrol of plant pathogens or for plant growth promotion.

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Degani, Ofir, Shlomit Dor, Dekel Abraham, and Roni Cohen. "Interactions between Magnaporthiopsis maydis and Macrophomina phaseolina, the Causes of Wilt Diseases in Maize and Cotton." Microorganisms 8, no. 2 (February 13, 2020): 249. http://dx.doi.org/10.3390/microorganisms8020249.

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Fungal pathogens are a significant threat to crops worldwide. The soil fungus, Magnaporthiopsis maydis, severely affects sensitive maize hybrids by causing the rapid wilting of plants at the maturity stage. Similarly, the soil fungus, Macrophomina phaseolina, develops in a variety of host plants, which leads to rot and plant mortality. The presence of both pathogens together in diseased cotton plants in Israel suggests possible interactions between them. Here, these relationships were tested in a series of experiments accompanied by real-time PCR tracking in maize and cotton. Despite the fact that neither of the pathogens was superior in a growth plate confrontation assay, their co-inoculum had a significant influence under field conditions. In maize sprouts and fully matured plants, infection by both pathogens (compared to inoculation with each of them alone) led to lesser amounts of M. maydis DNA but to increased amounts of M. phaseolina DNA levels. These results were obtained under a restricted water regime, while optimal water irrigation led to less pronounced differences. In water-stressed cotton sprouts, infection with both pathogens led to an increase in DNA amounts of each of the pathogens. Whereas the M. maydis DNA levels in the double infection remain high at the end of the season, a reduction in the amount of M. phaseolina DNA was observed. The double infection caused an increase in growth parameters in maize and cotton and decreased levels of dehydration in maize plants accompanied by an increase in yield production. Dehydration symptoms were minor in cotton under an optimal water supply. However, under a restricted water regime, the double infection abolished the harmful effect of M. phaseolina on the plants’ development and yield. These findings are the first report of interactions between these two pathogens in maize and cotton, and they encourage expanding the study to additional plant hosts and examining the potential involvement of other pathogens.

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Ramos Romero, Lucia, Dagmar Tacke, Birger Koopmann, and Andreas von Tiedemann. "First Characterisation of the Phoma Species Complex on Maize Leaves in Central Europe." Pathogens 10, no. 9 (September 18, 2021): 1216. http://dx.doi.org/10.3390/pathogens10091216.

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In the last decade, the cultivated area of maize has increased in Central Europe due to its high yield potential and diverse uses for feed and bio-energy. This has led to more intense maize cultivation, with narrowed crop rotations resulting in the increase in maize leaf diseases. During 2012 and 2013, an inventory of maize leaf spot diseases was carried out in various regions in Central Europe. In addition to the major leaf pathogens, isolates of Phoma-like species were obtained from oval to elliptical spots on leaves or found in lesions produced by other leaf pathogens. A total of 16 representative Phoma-like strains were characterised for their pathogenicity on maize leaves, for their morphological characteristics and with a phylogenetic analysis based on multilocus sequence analysis using part of the actin (ACT), calmodulin (CAL), β-tubulin (TUB), internal transcribed spacer (ITS) region of ribosomal DNA and large subunit ribosomal RNA (LSU) genes. The strains were grouped into four clades, and morphological studies supported this classification for most of them. Strains were compared with six reference Phoma-like species strains from the Westerndijk Fungal Biodiversity Institute collection reported to colonise maize. The pathogenic group of strains from our collection (after completion of Koch’s postulates) did not cluster with any of these species, indicating a different and novel Phoma-like species infecting maize leaves. To our knowledge, this is the first study dissecting the Phoma species complex on maize leaves in Central Europe.

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Reddy, Dr B. Rama Subba, Dr G. Bindu Madhavi, C. H. Sri Lakshmi, Dr K. Venkata Nagendra, and Dr R. Sridevi. "Detection of Disease in Maize Plant Using Deep Learning." Alinteri Journal of Agriculture Sciences 36, no. 2 (July 13, 2021): 82–88. http://dx.doi.org/10.47059/alinteri/v36i2/ajas21118.

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Agriculture is vital to the Indian economy as over 17 percent of total GDP and employs more than 60 percent of the population relies on agriculture. This research focuses on plant diseases as they create a major threat to food production as well as for small-scale farmer’s livelihood. Expert workers are employed in traditional farming to visually evaluate row by row to identify plant diseases, which is a time-consuming, labor-intensive activity that is potentially error-prone because it is done by humans. The aim of this research is to develop an automated detection model that uses a combination of image processing and deep learning techniques (Faster R-CNN+ResNet50) to analyze real-time images and detect and classify the three common maize plant diseases: Common Rust, Cercospora Leaf Spot, and Northern Leaf Blight. The proposed system achieved 91% accuracy and successfully detects three maize diseases.

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Xia, Zihao, Zhenxing Zhao, Zhiyuan Jiao, Tengzhi Xu, Yuanhua Wu, Tao Zhou, and Zaifeng Fan. "Virus-Derived Small Interfering RNAs Affect the Accumulations of Viral and Host Transcripts in Maize." Viruses 10, no. 12 (November 23, 2018): 664. http://dx.doi.org/10.3390/v10120664.

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RNA silencing is a conserved surveillance mechanism against invading viruses in plants, which involves the production of virus-derived small interfering RNAs (vsiRNAs) that play essential roles in the silencing of viral RNAs and/or specific host transcripts. However, how vsiRNAs function to target viral and/or host transcripts is poorly studied, especially in maize (Zea mays L.). In this study, a degradome library constructed from Sugarcane mosaic virus (SCMV)-inoculated maize plants was analyzed to identify the cleavage sites in viral and host transcripts mainly produced by vsiRNAs. The results showed that 42 maize transcripts were possibly cleaved by vsiRNAs, among which several were involved in chloroplast functions and in biotic and abiotic stresses. In addition, more than 3000 cleavage sites possibly produced by vsiRNAs were identified in positive-strand RNAs of SCMV, while there were only four cleavage sites in the negative-strand RNAs. To determine the roles of vsiRNAs in targeting viral RNAs, six vsiRNAs were expressed in maize protoplast based on artificial microRNAs (amiRNAs), of which four could efficiently inhibit the accumulations of SCMV RNAs. These results provide new insights into the genetic manipulation of maize with resistance against virus infection by using amiRNA as a more predictable and useful approach.

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Ruiz, M., E. A. Rossi, N. C. Bonamico, and M. G. Balzarini. "MULTI-TRAIT MODELS FOR GENOMIC REGIONS ASSOCIATED WITH MAL DE RÍO CUARTO AND BACTERIAL DISEASE IN MAIZE." Journal of Basic and Applied Genetics 32, Issue 1 (July 2021): 25–33. http://dx.doi.org/10.35407/bag.2020.32.01.03.

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Maize (Zea Mays L.) production has been greatly benefited from the improvement of inbred lines in regard to the resistance to diseases. However, the absence of resistant genotypes to bacteriosis is remarkable. The aim of the study was to identify genomic regions for resistance to Mal de Río Cuarto (MRC) and to bacterial disease (BD) in a diverse maize germplasm evaluated in the Argentinian region where MRC virus is endemic. A maize diverse population was assessed for both diseases during the 2019-2020 crop season. Incidence and severity of MRC and BD were estimated for each line and a genome wide association study (GWAS) was conducted with 78,376 SNP markers. A multi-trait mixed linear model was used for simultaneous evaluation of resistance to MRC and BD in the scored lines. The germplasm showed high genetic variability for both MRC and BD resistance. No significant genetic correlation was observed between the response to both diseases. Promising genomic regions for resistance to MRC and BD were identified and will be confirmed in further trials. Key words: maize disease; genome wide association study; SNP; multi-trait model

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Szőke, Csaba, Péter Bónis, Attila Vad, Attila Dobos, Györgyi Micskei, and L. Csaba Marton. "Describing Fusarium diseases on maize in 2013 using data from several production sites." Acta Agraria Debreceniensis, no. 62 (November 2, 2014): 60–64. http://dx.doi.org/10.34101/actaagrar/62/2167.

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As in other parts of the world, the frequency of weather extremes has increased greatly in Hungary in recent years. This means that maize production is faced with greater risks from all aspects: nutrient replacement, irrigation, plant protection. This is especially true of fusarium diseases. In a continental climate, the pathogens causing the most serious problems are species belonging to the Fusarium genus. They infect the ears, which – besides reducing the yield – poses considerable risk to both human and animal health due to the mycotoxins produced by them. Depending on which Fusarium species are dominant at a given location, changes can be expected in the level of infection and in the quality deterioration caused by the mycotoxins they produce. Fusarium spp. not only damages the maize ears but when pathogen attacks the stalk, the plant dies earlier, reducing grain filling and resulting in small, light ears. In addition, the stalks break or lodge, resulting in further yield losses from ears that cannot be harvested. The degree of infection is fundamentally determined by the resistance traits of the maize hybrids, but also a great role in that region Fusarium species composition as well.

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Zwonitzer, John C., Nathan D. Coles, Matthew D. Krakowsky, Consuelo Arellano, James B. Holland, Michael D. McMullen, Richard C. Pratt, and Peter J. Balint-Kurti. "Mapping Resistance Quantitative Trait Loci for Three Foliar Diseases in a Maize Recombinant Inbred Line Population—Evidence for Multiple Disease Resistance?" Phytopathology® 100, no. 1 (January 2010): 72–79. http://dx.doi.org/10.1094/phyto-100-1-0072.

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Southern leaf blight (SLB), gray leaf spot (GLS), and northern leaf blight (NLB) are all important foliar diseases impacting maize production. The objectives of this study were to identify quantitative trait loci (QTL) for resistance to these diseases in a maize recombinant inbred line (RIL) population derived from a cross between maize lines Ki14 and B73, and to evaluate the evidence for the presence genes or loci conferring multiple disease resistance (MDR). Each disease was scored in multiple separate trials. Highly significant correlations between the resistances and the three diseases were found. The highest correlation was identified between SLB and GLS resistance (r = 0.62). Correlations between resistance to each of the diseases and time to flowering were also highly significant. Nine, eight, and six QTL were identified for SLB, GLS, and NLB resistance, respectively. QTL for all three diseases colocalized in bin 1.06, while QTL colocalizing for two of the three diseases were identified in bins 1.08 to 1.09, 2.02/2.03, 3.04/3.05, 8.05, and 10.05. QTL for time to flowering were also identified at four of these six loci (bins 1.06, 3.04/3.05, 8.05, and 10.05). No disease resistance QTL was identified at the largest-effect QTL for flowering time in bin 10.03.

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Qiu, Yuting, Julian Cooper, Christopher Kaiser, Randall Wisser, Santiago X. Mideros, and Tiffany M. Jamann. "Identification of Loci That Confer Resistance to Bacterial and Fungal Diseases of Maize." G3: Genes|Genomes|Genetics 10, no. 8 (June 22, 2020): 2819–28. http://dx.doi.org/10.1534/g3.120.401104.

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Crops are hosts to numerous plant pathogenic microorganisms. Maize has several major disease issues; thus, breeding multiple disease resistant (MDR) varieties is critical. While the genetic basis of resistance to multiple fungal pathogens has been studied in maize, less is known about the relationship between fungal and bacterial resistance. In this study, we evaluated a disease resistance introgression line (DRIL) population for the foliar disease Goss’s bacterial wilt and blight (GW) and conducted quantitative trait locus (QTL) mapping. We identified a total of ten QTL across multiple environments. We then combined our GW data with data on four additional foliar diseases (northern corn leaf blight, southern corn leaf blight, gray leaf spot, and bacterial leaf streak) and conducted multivariate analysis to identify regions conferring resistance to multiple diseases. We identified 20 chromosomal bins with putative multiple disease effects. We examined the five chromosomal regions (bins 1.05, 3.04, 4.06, 8.03, and 9.02) with the strongest statistical support. By examining how each haplotype effected each disease, we identified several regions associated with increased resistance to multiple diseases and three regions associated with opposite effects for bacterial and fungal diseases. In summary, we identified several promising candidate regions for multiple disease resistance in maize and specific DRILs to expedite interrogation.

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Wisser, Randall J., Peter J. Balint-Kurti, and Rebecca J. Nelson. "The Genetic Architecture of Disease Resistance in Maize: A Synthesis of Published Studies." Phytopathology® 96, no. 2 (February 2006): 120–29. http://dx.doi.org/10.1094/phyto-96-0120.

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Fifty publications on the mapping of maize disease resistance loci were synthesized. These papers reported the locations of 437 quantitative trait loci (QTL) for disease (dQTL), 17 resistance genes (R-genes), and 25 R-gene analogs. A set of rules was devised to enable the placement of these loci on a single consensus map, permitting analysis of the distribution of resistance loci identified across a variety of maize germplasm for a number of different diseases. The confidence intervals of the dQTL were distributed over all 10 chromosomes and covered 89% of the genetic map to which the data were anchored. Visual inspection indicated the presence of clusters of dQTL for multiple diseases. Clustering of dQTL was supported by statistical tests that took into account genome-wide variations in gene density. Several novel clusters of resistance loci were identified. Evidence was also found for the association of dQTL with maturity-related QTL. It was evident from the distinct dQTL distributions for the different diseases that certain breeding schemes may be more suitable for certain diseases. This review provides an up-to-date synthesis of reports on the locations of resistance loci in maize.

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Rahmawati, Dian, Samrin, Baharudin, and Warda. "Major pests and diseases of maize and availability of control technology." IOP Conference Series: Earth and Environmental Science 484 (June 20, 2020): 012105. http://dx.doi.org/10.1088/1755-1315/484/1/012105.

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Zhang, Xihai, Yue Qiao, Fanfeng Meng, Chengguo Fan, and Mingming Zhang. "Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks." IEEE Access 6 (2018): 30370–77. http://dx.doi.org/10.1109/access.2018.2844405.

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Arora, Jatin, Utkarsh Agrawal, and Prerna Sharma. "Classification of Maize leaf diseases from healthy leaves using Deep Forest." Journal of Artificial Intelligence and Systems 2, no. 1 (2020): 14–26. http://dx.doi.org/10.33969/ais.2020.21002.

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Collins, Obiora Cornelius, and Kevin Jan Duffy. "Optimal control of maize foliar diseases using the plants population dynamics." Acta Agriculturae Scandinavica, Section B — Soil & Plant Science 66, no. 1 (September 28, 2015): 20–26. http://dx.doi.org/10.1080/09064710.2015.1061588.

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de Oliveira, Elizabeth, Paulo C. Magalhães, Reinaldo L. Gomide, Carlos A. Vasconcelos, Isabel R. P. Souza, Charles M. Oliveira, Ivan Cruz, and Robert E. Schaffert. "Growth and Nutrition of Mollicute-Infected Maize." Plant Disease 86, no. 9 (September 2002): 945–49. http://dx.doi.org/10.1094/pdis.2002.86.9.945.

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Maize bushy stunt phytoplasma (MBSP) and corn stunt spiroplasma (CSS) diseases are widespread in Brazil. The leafhopper Dalbulus maidis is the insect vector for these pathogenic mollicutes. The effects of these diseases on the development of maize plants and the possible interaction of soil water availability on these effects were evaluated in two experiments carried out on potted plants. Experiment 1 was carried out in a 2 × 4 factorial, where factor 1 corresponded to healthy and mollicute-infected plants and factor 2 to the maintenance of 40, 60, 80, and 100% of the total soil water availability. Leafhoppers collected from a field with high incidence of these diseases were used to inoculate plants with mollicutes. There were three treatments in experiment 2: healthy plants, plants infected with phytoplasma, and plants infected with spiroplasma. MBSP was predominant in experiment 1. The infected plants grew less and lowered nutrient uptake, in distinct proportions, indicating a differential effect of mollicutes on nutrient uptake independent of available soil water. Soil water availability did not significantly affect plant growth and nutrient uptake or mollicute infection. The results indicated that plants infected by mollicutes contained less protein than healthy plants. Experiment 2 showed a reduction in growth of plants infected with mollicutes and less nutrient uptake by spiroplasma-infected plants. The results showed a detrimental effect of the spiroplasma on Mg uptake. Both experiments showed more water retention by infected plants than by healthy ones. These experiments clearly demonstrated that reduced plant growth and nutrient uptake are major effects on plants infected with MBSP and CSS.

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Journal articles on the topic 'Maize diseases'

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