Relevant bibliographies by topics / Maize diseases

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Maize diseases'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Maize diseases.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Maize diseases"

1

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Maize diseases"

1

Obopile, Motshwari. "INTERACTIONS AMONG MAIZE PHENOLOGIES, TRANSGENIC BACILLUS THURINGIENSIS MAIZE AND SEED TREATMENT FOR MANAGEMENT OF PESTS AND DISEASES OF MAIZE." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1243020914.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Presello, Daniel A. "Studies on breeding of maize for resistance to ear rots caused by Fusarium spp. and on the occurrence of viruses in maize in eastern Canada." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=38260.

Full text
Abstract:
Responses from pedigree selection for resistance to gibberella ear rot were assessed in four maize (Zea mays L.) populations, two selected after inoculation of Fusarium graminearum (Schwabe) macroconidia into the silk channel and two selected after inoculation into developing kernels. Responses were significant in both populations selected for silk resistance and in one of the populations selected for kernel resistance. Selection was more effective in later generations and genetic gains were associated with among-family selection but not with within-family selection. Results obtained here indicate that responses to selection could be more efficiently obtained by applying high selection intensities in advanced generations, by managing earlier generations as bulks and by reducing the number of plants per family. In another experiment, a wide sample of Argentine maize germplasm was evaluated for silk and kernel resistance to gibberella ear rot and to fusarium ear rot (caused by F. verticillioides (Saccardo) Nirenberg [=F. moniliforme (Sheldon)]. Several entries exhibited disease resistance in comparison with local check hybrids, particularly for fusarium ear rot, the most prevalent ear rot in Argentina. Results obtained in this study suggested the presence of general mechanisms controlling silk and kernel resistance to both diseases. In a supplementary study, viral diseases were surveyed in maize fields from the provinces of Ontario and Quebec in 1999 and 2000. Barley yellow dwarf was found in 1999. Sugarcane mosaic, maize dwarf mosaic and wheat streak mosaic were found in 2000. These diseases were not important for grain-maize planted in May, the most prevalent kind of maize crop in these provinces. Some of these diseases, such as sugarcane maize mosaic and maize dwarf mosaic were found important only in maize fields planted during or after the month of June, and this is of commercial relevance only for sweet corn.
APA, Harvard, Vancouver, ISO, and other styles
3

Chauhan, Ramola. "A study of filamentous viruses in maize and smallgrains." Master's thesis, University of Cape Town, 1985. http://hdl.handle.net/11427/22013.

Full text
Abstract:
Bibliography: pages 175-184.
The occurrence of maize dwarf mosaic virus (MDMV) in field grown maize was investigated. For this purpose, maize showing mosiac symptoms was collected from different maize growing areas in South Africa by Prof. M.B. von Wechmar. These samples from Transvaal, Orange Free State and Natal were then investigated for the presence of MDMV and possible strains of this virus. Three virus isolates were purified and partially characterised. These isolates were serologically compared together with a fourth isolate SCMV 4975, obtained from the U.S., to establish strain relationships.
APA, Harvard, Vancouver, ISO, and other styles
4

Gomez, Luengo Rodolfo Gustavo. "Proteins and serological relationships of maize mosaic virus isolates and replication of the virus in Maize (Zea Mays L.) protoplasts /." The Ohio State University, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487327695621001.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Donahue, Patrick J. "Inheritance of reactions to gray leaf spot and maize dwarf mosaic virus in maize and their associations with physiological traits." Diss., Virginia Polytechnic Institute and State University, 1989. http://hdl.handle.net/10919/54518.

Full text
Abstract:
Gray leaf spot, caused by Cercospora zeae-maydis, can be a yield-limiting factor in maize where continuous minimum tillage practices are followed. Commercial corn hybrids were evaluated for response to gray leaf spot for seven years at two Virginia locations (Shenandoah and Wythe Counties) and one year at a third location in Virginia (Montgomery County). Yield losses, when comparing resistant to susceptible classes, were approximately 2,000 kg ha⁻¹ at Wythe County in 1982, 750 kg ha⁻¹ at Shenandoah County in 1984, and 2,150 kg ha⁻¹ at Montgomery County in 1988. The inheritance of reaction to gray leaf spot was studied using a 14 inbred diallel in Montgomery and Wythe Counties, Virginia in 1987 and 1988 planted in randomized complete block designs. Resistance was found to be highly heritable and controlled by additive gene action. Inbreds producing high yielding, resistant, and agronomically superior hybrids were identified (B68, NC250, Pa875, Va14, Va17, and Va85); and several hybrids between these lines had high levels of resistance, high yield, and good general agronomic characters (B68 x KB1250, KB1250 x Pa875, and NC250 x Pa875). Currently available inbreds could be used to produce hybrids with higher levels of resistance than hybrids currently available to growers, and these could serve as a basis for gray leaf spot breeding programs. Lesion size measurements were not correlated with disease scores. Late-season photosynthesis rates were associated positively with resistance. The hybrids of some inbreds were found to produce high levels of pigment (believed to be anthocyanins) around the gray leaf spot lesions. These did not limit the size of the individual lesion later in the season. Some pigment(s)-producing genotypes were found to be resistant when the pigment character was expressed. This type of resistance must prevent or inhibit infection of the leaf but not later colonization, once established. Maize dwarf mosaic virus (MDMV) also limits maize production in some areas where johnsongrass (Sorghum halepense L.) is a problem. Resistance to MDMV was found to be mainly additive and highly heritable. However, a strong specific combining ability component was found, indicating that the background of the material receiving resistance genes may have a strong effect on the expression of resistance. Inbreds capable of producing high-yielding, resistant, and agronomically acceptable hybrids are available (B68, NC250, A632, Pa875, Va17, and Va85); and several hybrids between these lines have high levels of resistance, high yield, and good general agronomic characters (B68 x KB1250, KB1250 x Pa875, and NC250 x Pa875).
Ph. D.
APA, Harvard, Vancouver, ISO, and other styles
6

Labuschagne, Alinke Heste. "Efficacy and crop tolerance of Stamina (pyraclostrobin) and Flite (triticonazole) seed treatment formulations against Fusarium, Pythium and Rhizoctonia soilborne diseases of maize." Diss., University of Pretoria, 2013. http://hdl.handle.net/2263/25702.

Full text
Abstract:
Maize (Zea mays L.) is a cereal crop grown throughout the world. It plays an important role in the diet of millions of African people due to its high yields per hectare, its ease of cultivation and adaptability to different areas, its versatile food uses and storage characteristics (Asiedu, 1989). Maize is a staple crop in Southern Africa where it accounts for 70% of total human intake of calories (Martin et al., 2000). Thus it is essential that maize can be sustainably produced in South Africa and that maize seeds are of the highest possible quality. Fungi rank as the second biggest cause of deterioration and loss of maize (Ominski et al., 1994). At the very early stages of seedling development, maize seedlings are attacked by fungi such as Pythium, Fusarium and Rhizoctonia spp., which cause severe diseases, including pre-emergence damping-off, which lead to yield losses (Dodd & White, 1999). These diseases can be effectively controlled by applying fungicidal seed treatments (Peltier et al., 2010). However, these seed treatments should be tested to ensure that they provide an acceptable level of control against the pathogens and that they do not have any negative effects on the germination and vigour of the maize seed. In Chapter 3 of this dissertation, three important fungal genera, namely Pythium, Fusarium and Rhizoctonia spp., were isolated from diseased maize plant samples and soil. The beet seed baiting method was used for Rhizoctonia sp. and the citrus leaf disk baiting method for Pythium sp. Fusarium sp. was isolated by means of serial dilution on a selective medium. The selective media used were agar containing chlorotetracycline hydrochloride and streptomycin sulfate for Rhizoctonia, pimaricin and vancomycin, PARP (pimaricin + ampicillin + rifampicin + pentachloronitrobenzene (PCNB) agar) for Pythium sp. and Rose Bengal Glyceraldehyde Urea (RBGU) for Fusarium sp. These fungal isolates, as well as some isolates revivedfrom the University of Pretoria’s culture collection and obtained from the Agricultural Research Council (ARC-PPRI), were used for pathogenicity trials conducted on maize in the between-paper method (BP), and in six-celled plastic seedling trays in the greenhouse (described in Chapter 5). In order to test the efficacy of Stamina, Flite and Celest® XL for controlling Pythium spp., Fusarium spp. and Rhizoctonia spp. in vitro, each of the three fungicides was added to PDA at concentrations of 1, 2 and 3ppm. In order to mirror the treatments used in other experiments, a combination of Stamina and Flite was also incorporatedinto PDA at concentrations of 1, 2 and 3ppm each. A 5mm2 block of each of the fungi was plated onto the centre of the media and incubated at 25C. The diameter of the fugal growth was measured at regular intervals depending on the rate of growth of the fungus. It was found that Celest® XL was very effective in controlling all three of these pathogens in vitro, confirming research done by Govender (2005), who found that Celest® XL effectively controlled these pathogens on maize. The combination of Stamina and Flite also controlled these pathogens although to a lesser extent. Research done by BASF in 2008 showed that Stamina is able to control Pythium, Fusarium and Rhizoctonia spp. Pyraclostrobin (the active ingredient of Stamina) has also been found to effectively control all three of these pathogens in numerous in vitro and in vivo experiments (Broders et al., 2007; Peltier et al., 2010; Solorzano & Malvick, 2011). In Chapter 4 of this dissertation, the effect of three different fungicides (Stamina, Flite and Celest® XL) on the germination and vigour of two Zea mays cultivars (Monsanto DKC78-15B and PANNAR 6Q308B) was assessed. This was achieved by carrying out a standard germination test, a cold soil test, short accelerated ageing and long-term storage tests according to the guidelines of the International Seed Testing Association (ISTA, 2012). It was found that none of the fungicides had a detrimental effect on either seed germination or vigour and no phytotoxic effects were observed. The combination of Stamina and Flite treatment also led to an increased percentage germination after the cold soil test when compared to the untreated control. This confirms the research of Govender (2005), who showed that Celest® XL had no negative effects on the germination or vigour of maize, and BASF (2008), which showed that Stamina could even lead to increased germination and an increased yield of maize under cold conditions when compared to an untreated control. Bradley et al. (2001) found that fungicide seed treatments do not affect the vigour and viability of maize seeds. Seeds treated with fludioxonil also showed an increased radicle length in some cases (Munkvold & O’Mara, 2002). Increased radicle length could indicate increased vigour of the seeds (Matthews & Khajeh-Hosseini,2006).
Dissertation (MSc (Agric))--University of Pretoria, 2013.
Microbiology and Plant Pathology
MSc (Agric)
Unrestricted
APA, Harvard, Vancouver, ISO, and other styles
7

Dhau, Inos. "Detection, identification, and mapping of maize streak virus and grey leaf spot diseases of maize using different remote sensing techniques." Thesis, University of Limpopo, 2019. http://hdl.handle.net/10386/2866.

Full text
Abstract:
Thesis (PhD. (Geography)) --University of Limpopo, 2019
Of late climate change and consequently, the spread of crop diseases has been identified as one of the major threat to crop production and food security in subSaharan Africa. This research, therefore, aims to evaluate the role of in situ hyperspectral and new generation multispectral data in detecting maize crop viral and fungal diseases, that is maize streak virus and grey leaf spot respectively. To accomplish this objective; a comparison of two variable selection techniques (Random Forest’s Forward Variable, (FVS) and Guided Regularized Random Forest: (GRRF) was done in selecting the optimal variables that can be used in detecting maize streak virus disease using in-situ resampled hyperspectral data. The findings indicated that the GRRF model produced high classification accuracy (91.67%) whereas the FVS had a slightly lower accuracy (87.60%) based on Hymap when compared to the AISA. The results have shown that the GRRF algorithm has the potential to select compact feature sub sets, and the accuracy performance is better than that of RF’s variable selection method. Secondly, the utility of remote sensing techniques in detecting the geminivirus infected maize was evaluated in this study based on experiments in Ofcolaco, Tzaneen in South Africa. Specifically, the potential of hyperspectral data in detecting different levels of maize infected by maize streak virus (MSV) was tested based on Guided Regularized Random Forest (GRRF). The findings illustrate the strength of hyperspectral data in detecting different levels of MSV infections. Specifically, the GRRF model was able to identify the optimal bands for detecting different levels of maize streak disease in maize. These bands were allocated at 552 nm, 603 nm, 683 nm, 881 nm, and 2338 nm. This study underscores the potential of using remotely sensed data in the accurate detection of maize crop diseases such as MSV and its severity which is critical in crop monitoring to foster food security, especially in the resource-limited subSaharan Africa. The study then investigated the possibility to upscale the previous findings to space borne sensor. RapidEye data and derived vegetation indices were tested in detecting and mapping the maize streak virus. The results revealed that the use of RapidEye spectral bands in detection and mapping of maize streak virus disease yielded good classification results with an overall accuracy of 82.75%. The inclusion of RapidEye derived vegetation indices improved the classification accuracies by 3.4%. Due to the cost involved in acquiring commercial images, like xviii RapidEye, a freely available Landsat-8 data can offer a new data source that is useful for maize diseases estimation, in environments which have limited resources. This study investigated the use of Landsat 8 and vegetation indices in estimating and predicting maize infected with maize streak virus. Landsat 8 data produced an overall accuracy of 50.32%. The inclusion of vegetation indices computed from Landsat 8 sensor improved the classification accuracies by 1.29%. Overally, the findings of this study provide the necessary insight and motivation to the remote sensing community, particularly in resource-constrained regions, to shift towards embracing various indices obtained from the readily-available and affordable multispectral Landsat-8 OLI sensor. The results of the study show that the mediumresolution multispectral Landsat 8-OLI data set can be used to detect and map maize streak virus disease. This study demonstrates the invaluable potential and strength of applying the readily-available medium-resolution, Landsat-8 OLI data set, with a large swath width (185 km) in precisely detecting and mapping maize streak virus disease. The study then examined the influence of climatic, environmental and remotely sensed variables on the spread of MSV disease on the Ofcolaco maize farms in Tzaneen, South Africa. Environmental and climatic variables were integrated together with Landsat 8 derived vegetation indices to predict the probability of MSV occurrence within the Ofcolaco maize farms in Limpopo, South Africa. Correlation analysis was used to relate vegetation indices, environmental and climatic variables to incidences of maize streak virus disease. The variables used to predict the distribution of MSV were elevation, rainfall, slope, temperature, and vegetation indices. It was found that MSV disease infestation is more likely to occur on low-lying altitudes and areas with high Normalised Difference Vegetation Index (NDVI) located at an altitude ranging of 350 and 450 m.a.s.l. The suitable areas are characterized by temperatures ranging from 24°C to 25°C. The results indicate the potential of integrating Landsat 8 derived vegetation indices, environmental and climatic variables to improve the prediction of areas that are likely to be affected by MSV disease outbreaks in maize fields in semi-arid environments. After realizing the potential of remote sensing in detecting and predicting the occurrence of maize streak virus disease, the study further examined its potential in mapping the most complex disease; Grey Leaf Spot (GLS) in maize fields using WorldView-2, Quickbird, RapidEye, and Sentinel-2 resampled from hyperspectral data. To accomplish this objective, field spectra were acquired from healthy, moderate and xix severely infected maize leaves during the 2013 and 2014 growing seasons. The spectra were then resampled to four sensor spectral resolutions – namely WorldView-2, Quickbird, RapidEye, and Sentinel-2. In each case, the Random Forest algorithm was used to classify the 2013 resampled spectra to represent the three identified disease severity categories. Classification accuracy was evaluated using an independent test dataset obtained during the 2014 growing season. Results showed that Sentinel-2 achieved the highest overall accuracy (84%) and kappa value (0.76), while the WorldView-2, produced slightly lower accuracies. The 608 nm and 705nm were selected as the most valuable bands in detecting the GLS for Worldview 2, and Sentinel-2. Overall, the results imply that opportunities exist for developing operational remote sensing systems for detection of maize disease. Adoption of such remote sensing techniques is particularly valuable for minimizing crop damage, improving yield and ensuring food security.
APA, Harvard, Vancouver, ISO, and other styles
8

Fandohan, Pascal. "Fusarium infection and mycotoxin contamination in preharvest and stored maize in Benin, West Africa." Thesis, University of Pretoria, 2004. http://hdl.handle.net/2263/24999.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Traut, Eduardo Jorge. "Bipolaris zeicola: physiological races, morphology and resistance on maize." Diss., Virginia Tech, 1993. http://hdl.handle.net/10919/40449.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Du, Min. "A greenhouse screening method for resistance to gray leaf spot in maize." Thesis, Virginia Tech, 1993. http://hdl.handle.net/10919/42953.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Maize diseases"

1

Obi, Ignatius U. Maize: Its agronomy, diseases, pests, and food values. Enugu: Optimal Computer Solutions, 1991.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

McGee, Denis C. Maize diseases: A reference source for seed technologists. St. Paul, Minn: APS Press, 1988.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Wallin, J. R. 1983 virus tolerance ratings of maize genotypes grown in Missouri. [Washington, D.C.]: U.S. Dept. of Agriculture, Agricultural Research Service, 1985.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Food and Agriculture Organization of the United Nations., ed. Tropical maize: Improvement and production. Rome: Food and Agricultural Organization of the United Nations, 2000.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

C, Alejandro Ortega. Insect pests of maize: A guide for field identification. Me xico, D.F., Me xico: International Maize and Wheat Improvement Center, 1987.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Throne, James Edward. A bibliography of maize weevils Sitophilus zeamais Metschulsky (Coleoptera: Curculionidae). [Washington, D.C.?]: U.S. Dept. of Agriculture, Agricultural Research Service, 1986.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Bürgi, Jürg. Insect-resistant maize: A case study of fighting the African stem borer. Wallingford, Oxfordshire, UK: CABI, 2009.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Workneh, Abraham Tadesse. Studies on some non-chemical insect pest management options on farm-stored maize in Ethiopia. Giessen: Fachverlag Köhler, 2003.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Daly, Carol Himsel. Maine coon cats: Everything about purchase, care, nutrition, reproduction, diseases, and behavior. Hauppauge, NY: Barron's, 1995.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Brewbaker, James L. The MIR (Maize inbred resistance) trials: Performance of tropical-adapted maize inbreds. Honolulu, Hawaii: HITAHR, College of Tropical Agriculture and Human Resources, University of Hawaii, 1989.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Maize diseases"

1

Gordon, D. T., and G. Thottappilly. "Maize and Sorghum." In Virus and Virus-like Diseases of Major Crops in Developing Countries, 295–336. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-007-0791-7_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Singh, A. K., V. B. Singh, J. N. Srivastava, S. K. Singh, and Anil Gupta. "Diseases of Maize Crops and Their Integrated Management." In Diseases of Field Crops: Diagnosis and Management, 105–40. Includes bibliographical references and indexes. | Content: Volume 1. Cereals, small millets, and fiber crops.: Apple Academic Press, 2020. http://dx.doi.org/10.1201/9780429321849-5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Chen, Jie, Murugappan Vallikkannu, and Valliappan Karuppiah. "Systemically Induced Resistance Against Maize Diseases by Trichoderma spp." In Trichoderma, 111–23. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3321-1_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Basandrai, Ashwani K., and Daisy Basandrai. "Brown Stripe Downy Mildew of Maize and Its Integrated Management." In Diseases of Field Crops: Diagnosis and Management, 141–51. Includes bibliographical references and indexes. | Content: Volume 1. Cereals, small millets, and fiber crops.: Apple Academic Press, 2020. http://dx.doi.org/10.1201/9780429321849-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Munkvold, Gary P. "Epidemiology of Fusarium diseases and their mycotoxins in maize ears." In Epidemiology of Mycotoxin Producing Fungi, 705–13. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-1452-5_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Agarwal, Rohit, and Himanshu Sharma. "Enhanced Convolutional Neural Network (ECNN) for Maize Leaf Diseases Identification." In Smart Innovations in Communication and Computational Sciences, 297–307. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5345-5_27.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Ma, Li, Helong Yu, Guifen Chen, Liying Cao, and Yueling Zhao. "Research on Construction and SWRL Reasoning of Ontology of Maize Diseases." In Computer and Computing Technologies in Agriculture VI, 386–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36137-1_45.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Khokhar, M. K., K. S. Hooda, P. N. Meena, R. Gogoi, S. S. Sharma, Rekha Balodi, and M. S. Gurjar. "Maize Diseases and Their Sustainable Management in India: Current Status and Future Perspectives." In Innovative Approaches in Diagnosis and Management of Crop Diseases, 179–219. New York: Apple Academic Press, 2021. http://dx.doi.org/10.1201/9781003187837-8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Guimaraes, Claudia Teixeira, and Jurandir Vieira de Magalhaes. "Recent molecular breeding advances for improving aluminium tolerance in maize and sorghum." In Molecular breeding in wheat, maize and sorghum: strategies for improving abiotic stress tolerance and yield, 318–24. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245431.0018.

Full text
Abstract:
Abstract Citrate transporters belonging to the multidrug and toxic compound extrusion (MATE) family of membrane transporters in sorghum and maize, SbMATE and ZmMATE1, respectively, play a major role in aluminium (Al) tolerance. However, these MATE members show regulatory differences, as well as peculiarities in their genetic effect and mode of action. These aspects, which are discussed in this chapter, have to be considered to design successful breeding programmes in order to achieve maximum Al tolerance and, consequently, to improve grain and biomass production in regions of the world with Al toxicity. As shown in this chapter, target genes with major effects and molecular tools are available for marker-assisted breeding for improving Al tolerance both in sorghum and maize. However, wide adaptation to acid soils should be sought by pyramiding genes controlling different traits such as drought tolerance, P acquisition, resistance to diseases and other stresses commonly found in each agroecological environment.
APA, Harvard, Vancouver, ISO, and other styles
10

Barman, Utpal, Diganto Sahu, and Golap Gunjan Barman. "A Deep Learning Based Android Application to Detect the Leaf Diseases of Maize." In Advances in Intelligent Systems and Computing, 275–86. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8061-1_22.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Maize diseases"

1

Da Rocha, Erik Lucas, Larissa Rodrigues, and João Fernando Mari. "Maize leaf disease classification using convolutional neural networks and hyperparameter optimization." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wvc.2020.13489.

Full text
Abstract:
Maize is an important food crop in the world, but several diseases affect the quality and quantity of agricultural production. Identifying these diseases is a very subjective and time-consuming task. The use of computer vision techniques allows automatizing this task and is essential in agricultural applications. In this study, we assess the performance of three state-of-the-art convolutional neural network architectures to classify maize leaf diseases. We apply enhancement methods such as Bayesian hyperparameter optimization, data augmentation, and fine-tuning strategies. We evaluate these CNNs on the maize leaf images from PlantVillage dataset, and all experiments were validated using a five-fold cross-validation procedure over the training and test sets. Our findings include the correlation between the maize leaf classes and the impact of data augmentation in pre-trained models. The results show that maize leaf disease classification reached 97% of accuracy for all CNNs models evaluated. Also, our approach provides new perspectives for the identification of leaf diseases based on computer vision strategies.
APA, Harvard, Vancouver, ISO, and other styles
2

Moawad, Nevien, and Abdelrahman Elsayed. "Smartphone Application for Diagnosing Maize Diseases in Egypt." In 2020 14th International Conference on Innovations in Information Technology (IIT). IEEE, 2020. http://dx.doi.org/10.1109/iit50501.2020.9299067.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Bylici, E. N. "Field assessment of mutant maize lines for resistance to diseases." In Problems of studying the vegetation cover of Siberia. TSU Press, 2020. http://dx.doi.org/10.17223/978-5-94621-927-3-2020-7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Sheikh, Md Helal, Tahmina Tashrif Mim, Md Shamim Reza, AKM Shahariar Azad Rabby, and Syed Akhter Hossain. "Detection of Maize and Peach Leaf diseases using Image Processing." In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2019. http://dx.doi.org/10.1109/icccnt45670.2019.8944530.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Borozan, Pantelimon, Simion Musteata, and Valentina Spinu. "Realizări şi perspective la programul de creare a hibrizilor de porumb timpuriu." In International Scientific Symposium "Plant Protection – Achievements and Prospects". Institute of Genetics, Physiology and Plant Protection, Republic of Moldova, 2020. http://dx.doi.org/10.53040/9789975347204.62.

Full text
Abstract:
The article presents results of maize improvement for northern zones during activity from the year of fondation of the laboratory. Use of created original inbred lines have permited to develop 23 registered hybrids, including eight in Belarus, two in Rusia and two in Moldova. They also performs research on assessing the combining ability capacity, cold tolerance, height plant density and tolerance for main diseases of inbred lines. They have been created 38 early hybrids with maturity index FAO 160-310 and more than 85 inbred lines from different germplasm groups.
APA, Harvard, Vancouver, ISO, and other styles
6

Hasan, Md Jahid, Md Shahin Alom, Umme Fatema Dina, and Mahmudul Hasan Moon. "Maize Diseases Image Identification and Classification by Combining CNN with Bi-Directional Long Short-Term Memory Model." In 2020 IEEE Region 10 Symposium (TENSYMP). IEEE, 2020. http://dx.doi.org/10.1109/tensymp50017.2020.9230796.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Overbeek, Marlinda Vasty, Yampi R. Kaesmetan, and Fenina Adline Twince Tobing. "Identification of Maize Leaf Diseases Cause by Fungus with Digital Image Processing (Case Study: Bismarak Village Kupang District - East Nusa Tenggara)." In 2019 5th International Conference on New Media Studies (CONMEDIA). IEEE, 2019. http://dx.doi.org/10.1109/conmedia46929.2019.8981843.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Nikolic, Valentina, Slađana Žilic, Marijana Simic, Milica Radosavljevic, Milomir Filipovic, and Jelena Srdic. "QUALITY PARAMETERS AND POTENTIALS OF UTILIZATION OF DIFFERENT MAIZE HYBRIDS FOR FOOD AND FEED." In XXVI savetovanje o biotehnologiji sa međunarodnim učešćem. University of Kragujevac, Faculty of Agronomy, 2021. http://dx.doi.org/10.46793/sbt26.495n.

Full text
Abstract:
Quality parameters of six maize hybrids created at the Maize Research Institute Zemun Polje were investigated in this study. Physical properties, kernel structure, and chemical composition of one yellow dent standard and five specialty maize hybrids of different grain color were analyzed. Whole-grain maize flour is naturally gluten-free which makes it suitable for persons suffering from celiac disease. Fiber, protein, and oil make maize grain an essential component for animal feed production. All maize hybrids showed favorable processing and nutritive characteristics which make them highly suitable for different uses.
APA, Harvard, Vancouver, ISO, and other styles
9

Nyasani, Johnson O. "Thrips as vectors of an emerging maize disease: A case study of maize chlorotic mottle virus." In 2016 International Congress of Entomology. Entomological Society of America, 2016. http://dx.doi.org/10.1603/ice.2016.105935.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Kai, Song, Liu Zhikun, Su Hang, and Guo Chunhong. "A Research of Maize Disease Image Recognition of Corn Based on BP Networks." In 2011 International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). IEEE, 2011. http://dx.doi.org/10.1109/icmtma.2011.66.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Maize diseases"

1

Houston, David R. Effect of harvesting regime on beech root sprouts and seedlings in a north-central Maine forest long affected by beech bark disease. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northeastern Research Station, 2001. http://dx.doi.org/10.2737/ne-rp-717.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Maize diseases' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography