Relevant bibliographies by topics / Maize leaf

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

Academic literature on the topic 'Maize leaf'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Maize leaf"

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Kleczkowski, Leszek A., and Douglas D. Randall. "Maize Leaf Adenylate Kinase." Plant Physiology 81, no. 4 (August 1, 1986): 1110–14. http://dx.doi.org/10.1104/pp.81.4.1110.

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Fernandez, D., and M. Castrillo. "Maize Leaf Rolling Initiation." Photosynthetica 37, no. 3 (November 1, 1999): 493–97. http://dx.doi.org/10.1023/a:1007124214141.

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Podestá, Florencio E., and Carlos S. Andreo. "Maize Leaf Phosphoenolpyruvate Carboxylase." Plant Physiology 90, no. 2 (June 1, 1989): 427–33. http://dx.doi.org/10.1104/pp.90.2.427.

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Zaefarian, Faezeh, Zahara Shakibafar, Mohammad Rezvani, and Hamid SALEHIAN. "Effect of cover crops on maize-velvet leaf competition: leaf area density and light interception." Acta agriculturae Slovenica 107, no. 2 (October 26, 2016): 409. http://dx.doi.org/10.14720/aas.2016.107.2.13.

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<p>Cover crops influence on canopy structure and light interception of maize (<em>Zea mays</em> L.) and velvetleaf (<em>Abutilon theophrasti</em> Medik), was studied in a field experiment. Treatments included planting of bean (<em>Phaseolus vulgaris</em> L.), soybean (<em>Glycine max </em>(L.) Merr.) and berseem clover (<em>Trifolium alexandrium</em> L.) as cover crops at the same date and 21 days after maize. Sole cropping of maize under weed- free and weedy conditions were also included in this experiment. All tested cover crops significantly reduced leaf area density and height of velvetleaf up to 50 %, while maize leaf area density increased in the presence of cover crops. Among cover crops, bean and soybean were the most effective in reducing velvetleaf leaf area density and height. Bean and soybean also strongly reduced absorbed light by velvetleaf by up to 80 % compared to clover. Maize grain yields were significantly influenced by cover crops planting in the inter row space. Compared to weeds free plots, only treatment with soybean as a cover crop resulted in similar maize grain yields, while maize intercropping with bean and clover significantly reduced maize yields. Delayed planting of cover crops, 21 day after maize, increased maize grain yield compared to cover crops and maize planting at the same time.</p>
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Girardin, Ph. "Leaf azimuth in maize canopies." European Journal of Agronomy 1, no. 2 (1992): 91–97. http://dx.doi.org/10.1016/s1161-0301(14)80006-3.

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CRAMER, GRANT R., and DANIEL C. BOWMAN. "Kinetics of Maize Leaf Elongation." Journal of Experimental Botany 42, no. 11 (1991): 1417–26. http://dx.doi.org/10.1093/jxb/42.11.1417.

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CRAMER, GRANT R. "Kinetics of Maize Leaf Elongation." Journal of Experimental Botany 43, no. 6 (1992): 857–64. http://dx.doi.org/10.1093/jxb/43.6.857.

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Cramer, Grant R. "Kinetics of Maize Leaf Elongation." Plant Physiology 100, no. 2 (October 1, 1992): 1044–47. http://dx.doi.org/10.1104/pp.100.2.1044.

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Streck, Nereu Augusto, Josana Andréia Langner, and Isabel Lago. "Maize leaf development under climate change scenarios." Pesquisa Agropecuária Brasileira 45, no. 11 (November 2010): 1227–36. http://dx.doi.org/10.1590/s0100-204x2010001100001.

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The objective of this work was to simulate maize leaf development in climate change scenarios at Santa Maria, RS, Brazil, considering symmetric and asymmetric increases in air temperature. The model of Wang & Engel for leaf appearance rate (LAR), with genotype-specific coefficients for the maize variety BRS Missões, was used to simulate tip and expanded leaf accumulated number from emergence to flag leaf appearance and expansion, for nine emergence dates from August 15 to April 15. LAR model was run for each emergence date in 100-year climate scenarios: current climate, and +1, +2, +3, +4 and +5°C increase in mean air temperature, with symmetric and asymmetric increase in daily minimum and maximum air temperature. Maize crop failure due to frost decreased in elevated temperature scenarios, in the very early and very late emergence dates, indicating a lengthening in the maize growing season in warmer climates. The leaf development period in maize was shorter in elevated temperature scenarios, with greater shortening in asymmetric temperature increases, indicating that warmer nights accelerate vegetative development in maize.
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Kalt-Torres, Willy, Phillip S. Kerr, Hideaki Usuda, and Steven C. Huber. "Diurnal Changes in Maize Leaf Photosynthesis." Plant Physiology 83, no. 2 (February 1, 1987): 283–88. http://dx.doi.org/10.1104/pp.83.2.283.

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Dissertations / Theses on the topic "Maize leaf"

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Cribb, Elizabeth J. "Golden2 gene function in maize leaf development." Thesis, University of Oxford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326139.

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Earl, Hugh J. "Estimating leaf photosynthesis in maize using chlorophyll fluorometry." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ31883.pdf.

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Clayton, Helen. "Carbohydrate oxidation in maize bundle sheath." Thesis, University of Cambridge, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.335719.

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So, Yoon-Sup. "Corn leaf aphid and polysora rust resistance in tropical maize." Thesis, University of Hawaii at Manoa, 2003. http://hdl.handle.net/10125/7012.

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This research includes two separate studies both of which incorporated generation mean analyses to interpret genetically the resistance to com leaf aphid and polysora rust in tropical maize. The first study focused on resistance to corn leaf aphid in tropical sweet corn inbred Hi38-71. An inoculation technique using hair-pin clip cages and infestation quantification method using digital image analysis were developed for this study. During the study, a heavy natural infestation of com leaf aphids occurred in a seed production nursery. Yield loss by ranged from 38.9% to 98.8%, with an average loss of 71.7%. The clip-cage method was effective in distinguishing resistant and susceptible plants under field conditions. Resistance to com leaf aphid from Hi38-71 appeared to be monogenic and recessive. Aphid reproduction and population growth were measured on four different genotypes of varying aphid tolerance. Aphids on Hi38-71 had poorest performance over all aspects of growth and reproduction examined. Difference in number of progenies produced and days to 50% mortality appeared to account for most of the difference observed in the genetic study. The second study estimated genetic parameters for polysora rust resistance in Hi38-71. Hi38-71 exhibited moderately high resistance to polysora rust as well as resistance to com leaf aphid. Generation mean analysis showed that epistatic interactions of [aa] and [dd] along with simple dominance and additive gene effects were involved in controlling resistance in Hi38-71 to polysora rust. It is concluded that polysora resistance breeding cannot be based on selection of a single parent but a hybrid-breeding or reciprocal recurrent selection approach appears justified. The tropical sweet corn inbred, Hi38-71 is a sib line of Hi38 which was bred from a bt-1 conversion of AA8sh2. AA8sh2 was studied for its resistance to corn leaf aphid in 1970's in Hawaii and was converted to common rust resistance, Rd1-D which broke down due to evolved racial variation of the pathogen. Hi38-71 is thus of particular value in sweet corn breeding for tropical regions. This is due not only to its resistance to corn leaf aphid and polysora, but to its high sweet corn qualities and generally good combining abilities.
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Lehmensiek, Anke. "Genetic mapping of gray leaf spot resistance genes in maize." Thesis, Stellenbosch : Stellenbosch University, 2000. http://hdl.handle.net/10019.1/51776.

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Thesis (PhD)--Stellenbosch University, 2000.
ENGLISH ABSTRACT: Gray leaf spot (GLS) of maize, caused by the fungus Cercospora zeae-maydis, can reduce grain yields by up to 60% and it is now recognized as one of the most significant yield-limiting diseases of maize in many parts of the world. The most sustainable and long-term management strategy for GLS will rely heavily on the development of high-yielding, locally adapted GLS resistant hybrids. Molecular markers could be useful to plant breeders to indirectly select for genes affecting GLS resistance and to identify resistance genes without inoculation and at an early stage of plant development. Only two studies in the USA have examined quantitative trait loci (QTL) association with GLS resistance. The aim of this study was to map GLS resistance genes in a resistant Seed Co LTD, Zimbabwean inbred line. Molecular markers linked to the GLS resistance QTL were identified by using the amplified fragment length polymorphism (AFLP) technique together with bulked segregant analysis. Eleven polymorphic AFLP fragments were identified and converted to sequence-specific PCR (polymerase chain reaction) markers. Eight of the 11 converted AFLP markers were added to the maize marker database of the University of Stellenbosch. Five of the 8 converted AFLP markers were polymorphic between the resistant and the susceptible parent. They were amplified on the DNA of 230 plants of a segregating F2 population and linkage analysis was performed with MAPMAKER/EXP. Two linkage groups consisting of two markers each, with a linkage distance of 10.4 cM (LOD 22.83) and 8.2 cM (LOD 55.41) between the two markers, were identified. QTL mapping with MAPMAKER/QTL confirmed the presence of QTL in both linkage groups. Two publicly available recombinant inbred families (Burr et a/., 1988) were used to localize the converted AFLP markers on the genetic map of maize. The QTL, which were identified with the AFLP markers, were mapped to chromosomes 1 and 5. Another AFLP marker was mapped to chromosome 2 and a further to chromosome 3. To obtain more precise localizations of the QTL on chromosomes 1 and 5, sequence-tagged site markers and microsatellite markers were used. The markers were amplified on the DNA of the 230 plants of the F2 population and linkage analysis was performed with MAPMAKER/EXP. The order of the markers was in agreement with the UMC map of the Maize Genome Database. Interval mapping using MAPMAKERlQTL and composite interval mapping using QTL Cartographer were performed. The QTL on chromosome 1 had a LOD score of 21 and was localized in bin 1.05/06. A variance of 37% was explained by the QTL. Two peaks were visible for the QTL on chromosome 5, one was localized in bin 5.03/04 and the other in bin 5.05/06. Both peaks had a LOD score of 5 and 11% of the variance was explained by the QTL. To test the consistency of the detected QTL, the markers flanking each QTL were amplified on selected plants of two F2 populations planted in consecutive years and regression analysis was performed. Both the QTL on chromosome 1 and the QTL on chromosome 5 were detected in these populations. Furthermore, the presence of a QTL on chromosome 3 was confirmed with these populations. A variance of 8 -10% was explained by the QTL on chromosome 3. In this study, a major GLS resistance QTL was thus mapped on chromosomes 1 and two minor GLS resistance QTL were mapped on chromosomes 3 and 5 using a resistant Seed Co LTD, Zimbabwean inbred line. Markers were identified which could be used in a marker-assisted selection program to select for the GLS resistance QTL.
AFRIKAANSE OPSOMMING: Grys blaarvlek (GBV) van mielies, veroorsaak deur die swam Cercospora zeaemaydis, kan graanopbrengs met tot 60% verlaag en word beskou as een van die vernaamste opbrengs-beperkende siektes wêreldwyd. Die toepaslikste langtermyn stragtegie vir GBV beheer sal wees om plaaslike mieliebasters met hoë opbrengs en GBV weerstand te ontwikkel. Molekulêre merkers kan nuttig deur plantetelers gebruik word om weerstandsgene te selekteer. Seleksie is moontlik in die afwesigheid van inokolum en op 'n vroeë stadium van plant ontwikkeling. Slegs twee vorige studies (in die VSA) het kwantitatiewe-kenmerk-Iokusse (KKL), vir GBVweerstand ondersoek. Die doel van hierdie studie was om die GBV weerstandsgene in 'n weerstandbiedende ingeteelde lyn (Seed Co BPK, Zimbabwe) te karteer. Molekulêre merkers gekoppel aan die GBV weerstands KKL is geïdentifiseer deur gebruik te maak van die geamplifiseerde-fragmentlengte-polimorfisme- (AFLP-) tegniek en gebulkte-segregaat-analise. Elf polimorfiese merkers is geïdentifiseer en omgeskakel na volgorde-spesifieke PKR (polimerase kettingreaksie) merkers. Agt van die elf omgeskakelde AFLP-merkers is by die mieliemerker databasis van die Universiteit van Stellenbosch gevoeg. Vyf van die 8 omgeskakelde AFLP-merkers was polimorfies tussen die bestande en vatbare ouers. Hulle is geamplifiseer op die DNA van 230 plante van 'n segregerende F2-populasie en is gebruik in 'n koppelingstudie met MAPMAKER/EXP. Twee koppelingsgroepe, elk bestaande uit twee merkers, met onderskeidelik koppelingsafstande van 10.4 eM (LOD 22.83) en 8.2 eM (LOD 55.41) tussen die merkers, is geïdentifiseer. KKL-kartering het getoon dat KKL in albei koppelingsgroepe aanwesig is. Twee kommersieël beskikbare, rekombinant-ingeteelde families (Burr et aI., 1988) is gebruik om die omgeskakelde AFLP-merkers op die mielie genetiese kaart te plaas. Die KKL wat met die AFLP-merkers geïdentifiseer is, is gekarteer op chromosome 1 en 5. 'n Verdere AFLP-merker is op chromosoom 2 gekarteer en 'n ander op chromosoom 3. Ten einde die KKL op chromosome 1 en 5 meer akkuraat te karteer, is volgordege- etikeerde en mikrosatelliet merkers gebruik. Die merkers is geamplifiseer op die DNA van die 230 plante van die F2-populasie en koppelings-analises is uitgevoer. Die volgorde van die merkers was dieselfde as die van die UMC-kaart in die Mielie Genoom Databasis. Interval kartering met MAPMAKER/QTL en komposiet interval kartering met QTL Cartographer is uitgevoer. Die KKL op chromosoom 1 het 'n LOD-telling van 21 gehad en is in bin 1.05/06 geplaas. Die KKL was verantwoordelik vir 37% van die variansie. Twee pieke was onderskeibaar vir die KKL op chromosoom 5, een in bin 5.03/04 geleë en die ander in bin 5.05/06. Elke piek het 'n LOD-telling van 5 gehad en die twee KKL was verantwoordelik vir 11% van die variansie. Om die herhaalbaarheid van die effek van die KKL te toets is die merkers naaste aan elke KKL geamplifiseer op geselekteerde plante van twee F2-populasies wat in opeenvolgende jare geplant is. Regressie analise is op die data gedoen. Beide die KKL op chromosoom 1 en die KKL op chromosoom 5 kon in hierdie populasies geïdentifiseer word. Verder kon die aanwesigheid van 'n verdere KKL op chromosoom 3 in hierdie populasies bevestig word. Laasgenoemde KKL was verantwoordelik vir 8-10% van die totale variansie. In hierdie studie is daar dus 'n hoof GBV-weerstands KKL gekarteer op chromosoom 1 en twee kleiner GBV-weerstands KKL gekarteer op chromosome 3 en 5. Merkers is geïdentifiseer wat moontlik in merker-gebaseerdetelingsprogramme gebruik kan word om plante te selekteer wat die GBVweerstands KKL het.
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Gallagher, Kimberly L. "Analysis of asymmetric cell divisions in the maize leaf epidermis /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2000. http://wwwlib.umi.com/cr/ucsd/fullcit?p3007134.

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Roth, Ronelle. "Phenotypic characterization of maize bundle sheath defective mutants." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339349.

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

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Mwangi, Symon Munanda. "Status of Northen Leaf Blight, Phaeosphaeria maydis Leaf Spot, Southern Leaf Blight, Rust, Maize Streak Virus and Physiologic Specialization of Exserohilum turcicum in Kenya." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/26093.

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Exserohilum turcicum, Phaeosphaeria maydis, Bipolaris maydis, Puccinia polysora, Puccinia sorghi and maize streak virus were the most serious maize pathogens found in Kenya during a 1995/96 survey of 65 farms representing all the agro-ecological zones. Exserohilum turcicum - induced blight was observed in all maize producing areas with disease incidence reaching over 45%. Lesions were sometimes over 400 mm long by 25 mm in width. Southern leaf blight caused by Bipolaris maydis was most severe in the Kenyan maize granary located in the highlands west of the Rift Valley where it occurred alone or with Phaeosphaeria maydis leaf spot. Incidences of over 75% were recorded for southern leaf blight and Phaeosphaeria maydis leaf spot in western Kenya and over 85% for Phaeosphaeria maydis leaf spot in Thika, Nyeri, Muranga and Kirinyaga Districts of Central Province. Common rust caused by Puccinia sorghi was equally severe in the areas of Molo, Njoro and Bahati in Nakuru District with severity levels of over 65%. Lowland rust caused by Pucinia polysora was important in the coastal region with over 75% disease severity in Kwale District. Traces of lowland rust were observed in Kirinyaga District. Maize streak virus was severe in the Lake Victoria region of western Kenya, central areas of Kenya and in the Coast. This disease was severe where Napier grass (Pennisetum purpureum) with streak like symptoms was near the infected maize plots. Individual cultures of Exserohilum turcicum were not appreciably different in appearance but varied in growth rates at 22oC. A total of nineteen isolates of Exserohilum turcicum were classified into races using the Ht gene bearing maize differential inbred lines. Three isolates were race 0, three were race 1, one was race 2, one was race 3, two isolates were race 12, and eight did not fit in the known classification. These were named and grouped as k1, k2 and k3. The k3 race was virulent to HtN gene only. Four Kenyan and two American lines were inoculated with eight isolates of Exserohilum turcicum. All the Kenyan lines were susceptible to the isolates used. Tables of means comparisons and figures for disease occurrence and severity are provided. This is the first time disease prevalence and severity has been documented for maize in Kenya.
Ph. D.
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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.

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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.
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Books on the topic "Maize leaf"

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Hossain, Mohammad Anwar, Mobashwer Alam, Saman Seneweera, Sujay Rakshit, and Robert Henry, eds. Molecular breeding in wheat, maize and sorghum: strategies for improving abiotic stress tolerance and yield. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245431.0000.

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Abstract This book contains 29 chapters focusing on wheat, maize and sorghum molecular breeding. It aims to contribute the latest understandings of the molecular and genetic bases of abiotic stress tolerance, yield and quality improvement of wheat, maize and sorghum to develop strategies for improving abiotic stress tolerance that will lead to enhance productivity and better utilization of natural resources to ensure food security through modern breeding.
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"But don't all religions lead to God?": Navigating the multi-faith maze. Vereeniging: Christian Art, 2002.

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Michael, Green. But don't all religions lead to God?: Navigating the multi-faith maze. Leicester: Inter-Varsity Press, 2002.

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Michael, Green. "But don't all religions lead to God?": Navigating the multi-faith maze. Grand Rapids, Mich: Baker Books, 2002.

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Michael, Green. But don't all religions lead to God?: Navigating the multi-faith maze. Grand Rapids, Mich: Baker Books, 2002.

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Alone and invisible no more: How grassroots community action and 21st century technologies can empower elders to stay in their homes and lead healthier, happier lives. White River Junction, Vt: Chelsea Green Pub., 2011.

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Boudreau, Mark Alan. Effects of intercropping beans with maize on angular leaf spot and rust of beans. 1991.

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Modeling Uptake and Translocation of Lead (PB) in Maize for the Purposes of Phyutoextraction. Storming Media, 1997.

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But Don't All Religions Lead to God?: Navigating the Multi-faith Maze. Sovereign World, 2002.

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Archer, Richard. Forward Steps. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190676643.003.0008.

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Reform in all its various coats became somewhat more respectable, but most of all, African Americans were learning how to work the system and were taking the lead in fighting for equal rights. Black Rhode Islanders gained voting rights. Boston's African American community with significant white support kept George Latimer from being reenslaved and in the process prompted the creation of personal liberty laws in every New England state but Maine. By the mid-1840s all of New England north of Rhode Island and Connecticut, with the single exception of Boston, had integrated schools. Black communities with white allies and increasingly sympathetic towns and cities prevailed. That would not have happened in a white supremacist society. New England certainly had its white supremacists, but their number was small. White supremacists were racists, but racists were not necessarily white supremacists.
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Book chapters on the topic "Maize leaf"

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Freeling, Michael, and Barbara Lane. "The Maize Leaf." In The Maize Handbook, 17–28. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2694-9_3.

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Sylvester, Anne W., and Laurie G. Smith. "Cell Biology of Maize Leaf Development." In Handbook of Maize: Its Biology, 179–203. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-79418-1_10.

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Foster, Toshi M., and Marja C. P. Timmermans. "Axial Patterning of the Maize Leaf." In Handbook of Maize: Its Biology, 161–78. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-79418-1_9.

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Budde, Raymond J. A., and Raymond Chollet. "In Vitro Phosphorylation of Maize Leaf Phosphoenolpyruvate Carboxylase." In Progress in Photosynthesis Research, 503–6. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-017-0516-5_107.

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Walton, J. D. "Molecular Basis of Specificity in Maize Leaf Spot Disease." In Advances in Molecular Genetics of Plant-Microbe Interactions, Vol. 2, 313–23. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-017-0651-3_34.

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Singamsetty, Phani Kumar, G. V. N. D. Sai Prasad, N. V. Swamy Naidu, and R. Suresh Kumar. "Maize Leaf Disease Detection and Classification Using Deep Learning." In Artificial Intelligence in Mechanical and Industrial Engineering, 87–102. First edition. | Boca Raton : CRC Press, 2021. | Series: Artificial intelligence (AI) in engineering: CRC Press, 2021. http://dx.doi.org/10.1201/9781003011248-6.

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Narayankar, Prashant, and Priyadarshini Patil. "Improved Nutrition Management in Maize by Analyzing Leaf Images." In Information and Communication Technology for Competitive Strategies (ICTCS 2020), 65–73. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0882-7_4.

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Zhao, Yu-Xia, Ke-Ru Wang, Zhong-Ying Bai, Shao-Kun Li, Rui-Zhi Xie, and Shi-Ju Gao. "Research of Maize Leaf Disease Identifying Models Based Image Recognition." In Crop Modeling and Decision Support, 317–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01132-0_35.

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Panigrahi, Kshyanaprava Panda, Himansu Das, Abhaya Kumar Sahoo, and Suresh Chandra Moharana. "Maize Leaf Disease Detection and Classification Using Machine Learning Algorithms." In Advances in Intelligent Systems and Computing, 659–69. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2414-1_66.

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Freeling, Michael, Deverie K. Bongard-Pierce, Nicholas Harberd, Barbara Lane, and Sarah Hake. "Genes Involved in the Patterns of Maize Leaf Cell Division." In Plant Gene Research, 41–62. Vienna: Springer Vienna, 1988. http://dx.doi.org/10.1007/978-3-7091-6950-6_3.

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Conference papers on the topic "Maize leaf"

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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.

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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.
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Emil, Georgescu. "Maize leaf weevil (Tanymecus dilaticollisGyll): Present situation in Romania." In 2016 International Congress of Entomology. Entomological Society of America, 2016. http://dx.doi.org/10.1603/ice.2016.110355.

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Varsani, Suresh. "Maize defense responses to phloem sap-sucking corn leaf aphid." In 2016 International Congress of Entomology. Entomological Society of America, 2016. http://dx.doi.org/10.1603/ice.2016.115425.

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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.

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Gu, Xiaohe, Lizhi Wang, Xiaoyu Song, and Xingang Xu. "Estimating leaf nitrogen accumulation in maize based on canopy hyperspectrum data." In SPIE Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2016. http://dx.doi.org/10.1117/12.2241152.

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ARAúJO, G. S., I. M. BARBOSA, S. O. PAULA, E. C. MARQUES, E. GOMES FILHO, and J. T. PRISCO. "PHOTOSYNTHETIC PERFORMANCE OF MAIZE PLANTS UNDER SALINITY AS AFFECTED BY LEAF H2O2 PRIMING." In IV Inovagri International Meeting. Fortaleza, Ceará, Brasil: INOVAGRI/ESALQ-USP/ABID/UFRB/INCT-EI/INCTSal/INSTITUTO FUTURE, 2017. http://dx.doi.org/10.7127/iv-inovagri-meeting-2017-res5130897.

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Lv, Jie, and Zhenguo Yan. "Retrieval of chlorophyll content in maize from leaf reflectance spectra using wavelet analysis." In International Symposium on Optoelectronic Technology and Application 2014, edited by Jannick P. Rolland, Changxiang Yan, Dae Wook Kim, Wenli Ma, and Ligong Zheng. SPIE, 2014. http://dx.doi.org/10.1117/12.2073113.

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Jinyong, Wang, Tan Wenrong, Hou Shuaimin, Wang Yang, and Zhang Hongna. "Research on Quantification Method of Maize Leaf Phenotype Parameters Based on Machine Vision." In 2020 International Symposium on Computer Engineering and Intelligent Communications (ISCEIC). IEEE, 2020. http://dx.doi.org/10.1109/isceic51027.2020.00026.

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Feng, Haikuan, Haojie Pei, Fuqin Yang, Guijun Yang, Zhenhai Li, Yang Xiaodong, Huiling Long, and Xiuliang Jin. "Estimation of leaf nitrogen content of maize based on Akaike's information criterion in Beijing." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8128140.

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Reiser, D., A. Kamman, M. Vázquez Arellano, and H. W. Griepentrog. "Using terrestrial photogrammetry for leaf area estimation in maize under different plant growth stages." In 12th European Conference on Precision Agriculture. The Netherlands: Wageningen Academic Publishers, 2019. http://dx.doi.org/10.3920/978-90-8686-888-9_41.

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Reports on the topic "Maize leaf"

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Maps showing the distribution of copper, lead, and zinc in stream sediments, Sherbrooke and Lewiston 1 degree by 2 degrees Quadrangles, Maine, New Hampshire, and Vermont. US Geological Survey, 1990. http://dx.doi.org/10.3133/i1898b.

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