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

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.