Doctoral Degrees (Environmental Biology)
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Item Application of unmanned aerial systems for crop discrimination in smallholder farms.(2025) Mafuratidze, Pride.; Mutanga, Onisimo.; Masocha, Mhosisi.Agriculture is the cornerstone of global food security, serving as humanity’s principal source of sustenance and the primary supplier of critical crops. A crucial challenge facing society is ensuring food security for a rapidly growing population, projected to exceed nine billion by 2050. With limited opportunities to expand arable land, improving agricultural productivity has become indispensable to meet escalating global food demand. Thus, there is a need for robust, precise, holistic agricultural intelligence systems to monitor and optimise crop production. This is particularly so, in regions characterised by heterogeneous smallholder farming systems dominated by mixed cropping. The ability to identify and monitor individual crop types is fundamental for optimising resource allocation, informing targeted interventions, and ultimately enhancing agricultural productivity. Unfortunately, the use of traditional groundbased methods for crop identification has been deemed labour-intensive, time-consuming, and spatially limited, rendering them inadequate for large-scale or frequently updated crop assessments. Thus, the efficacy of remote sensing technologies has been proven in acquiring synoptic and multi-temporal data that is crucial for agricultural monitoring and management. Among the suite of remote sensing platforms, unmanned aerial systems (UASs) have garnered significant attention due to their capacity for near-real-time data acquisition at high spatial resolutions. Equipped with increasingly sophisticated yet miniaturised and lightweight sensors, UAS offers a flexible and cost-effective alternative to traditional aerial and satellite imagery, particularly for localised agricultural applications. The advancements in geospatial technologies have facilitated critical data collection on various farm tasks, with crop discrimination and classification as key focus areas. However, despite the evident potential of UAS in agriculture, several bottlenecks have been identified, including lack of comprehensive information regarding optimal UAS configuration, sensor characteristics tailored for specific crop discrimination, and robust data processing and analytical methodologies applicable across diverse cropping systems. Given this background, this study sought to (i) systematically review the current state, challenges and opportunities in the application of unmanned aerial systems for crop discrimination, (ii) determine the optimal field parameters, specifically the number of crop species and crop row widths, that facilitate accurate crop discrimination and (iii) develop techniques that distinguish crop types in a mixed cropping setting, owing to the flexibility and cost-effectiveness of UASs particularly for localised agricultural applications. This thesis addresses the overarching challenge of achieving accurate and reliable crop discrimination, explicitly focusing on the prevalent mixed-cropping system of maize (Zea mays) and soybean (Glycine max), which are of significant economic and nutritional importance in regions of sub-Saharan Africa. The thesis focused on multiple investigations employing a range of remote sensing data modalities and analytical techniques to tackle the complexities inherent in distinguishing spectrally and structurally similar crops within heterogeneous agricultural environments. The first objective was to systematically review the current state, challenges and opportunities in the application of unmanned aerial systems for crop discrimination. This was followed by examining the spectral separability of maize and soybean across different growth stages using hyperspectral data. Thirdly, the thesis evaluated the utility of spectral, textural and morphological features derived from UAS-based RGB imagery to distinguish maize and soybean from other objects. This was followed by developing a novel technique for shadow detection in RGB datasets. Lastly, the thesis developed a hybrid approach by integrating segmentation and pixel-based classification to provide a comprehensive understanding of effective remote sensing strategies for enhanced crop discrimination in mixedcropping systems. The ultimate goal is to contribute to advancing precision agriculture practices, particularly in resource-constrained settings, where accurate and timely information on crop distribution is paramount for sustainable agricultural development. This explores different facets of crop discrimination in mixed-cropping systems, which are a characteristic of smallholder farming systems in most developing countries. The second objective sought to investigate the spectral separability of maize and soybean at different phenological stages based on field experiments. To achieve this, hyperspectral data spanning the visible to near-infrared spectrum (400–1100 nm) were employed to evaluate the spectral signatures of these two crops across five critical growth stages. The integration of statistical analysis (ANOVA), distance (Jeffries-Matusita distance) and divergence metrics (Transform Divergence, Kullback-Leibler Divergence), and machine learning algorithms (Partial Least Squares-Discriminant Analysis (PLS-DA)) provided a robust framework for optimising band selection and identifying critical phenological stages for discrimination. The key findings of this study revealed that peak spectral separability occurred during the reproductive stages (85– 110 days after planting), with the red spectral region (600–700 nm) exhibiting maximum divergence, attributed to differences in chlorophyll dynamics. Notably, PLS-DA achieved nearperfect classification accuracy (100% F1-score) at the mid-grain filling stage (DAP 85), highlighting the efficacy of leveraging red-edge (680–750 nm) and near-infrared (700–1100 m) bands during this period. Conversely, minimal separability was observed during early vegetative stages due to spectral overlap. This research underscores the need to consider phenological timing and specific spectral regions for effective crop discrimination using hyperspectral data, offering valuable insights for designing targeted remote sensing surveys. The third objective leveraged the increasing accessibility and affordability of UAS-based RGB imagery to evaluate the utility of spectral, textural, and morphological features for distinguishing maize and soybean in a mixed-cropping environment. High-spatial-resolution RGB images were captured during the tasselling stage of maize (48 days after planting) using a DJI Matrice 300 drone. Due to persistent cloud cover and rainfall during the summer, data acquisition was constrained; consequently, only DAP48 observations were obtained during the reproductive and maturity stages. By extracting a comprehensive set of 26 variables encompassing spectral indices, textural features, and morphological transformations, the study employed a random forest (RF) algorithm for supervised classification. The results emphatically demonstrated the superior performance of morphological features, achieving the highest classification accuracy (0.93) and F1-score (92%), followed by a combination of textural and morphological features. Spectral features alone proved to be the least effective. Morphological features, capturing canopy structure and plant geometry, outperformed spectral and textural traits, highlighting the limitations of spectral-only approaches in mixed-cropping systems. Although these features focusing on the structural and geometrical features of maize and soybean were successful, the results revealed that RGB datasets in smallholder farms were compromised by shadows, which disproportionately increase spectral overlap. To address the problem of shadows that are prevalent due to mixed cropping with varying plant heights, particularly in smallholder farming systems, the fourth objective developed a novel hue-intensity-green-blue (HIGB) difference technique. The performance of this new technique was rigorously compared against established methods (C3 and normalised saturation-value difference index) using RGB datasets from experimentally manipulated maize and soybean mixtures. The HIGB technique, based on the differences between hue and intensity and the green and blue channels, consistently outperformed the benchmark models (C3 and NSVDI) across various shadow conditions, achieving overall accuracies ranging from 77% to 95%. This robust performance, even in scenarios with dark or obscured shadows, underscored the practical utility of the HIGB technique for improving the reliability of crop discrimination efforts using RGB imagery. The HIGB technique performs robustly under varying lighting conditions, nderscoring its value as a critical preprocessing tool for improving crop discrimination. Furthermore, the thesis proposed an alternative light intensity ratio-based (LIRB) approach for shadow removal using RGB imagery. This method is applicable in areas where shadow pixels are sparse; however, it did not fully meet expectations. The approach struggled to reconstruct or eliminate dense shadows, resulting in the introduction of blurry artefacts. These artefacts significantly compromised the overall objective of accurately detecting actual crop acreage within a mixed cropping system. By understanding the limitations of LIRB, the last chapter focused on developing a hybrid classification framework integrating region-based segmentation and pixel-based machine learning. This approach was proposed to tackle the spectral and structural complexity of heterogeneous agro-ecological landscapes by focusing on vegetation pixels only. This method leverages simple linear iterative clustering (SLIC) superpixels to group spectrally similar pixels into meaningful and targeted regions, followed by extracting texture and structural features from these segments. These multi-faceted features were then used to train robust machine learning classifiers: Random Forest and Extreme Gradient Boosting. The experimental results demonstrated remarkably high detection accuracy, with precision, recall, and F1-scores exceeding 0.98 for both classifiers. Feature contribution analysis revealed that mean intensity and standard deviation features derived from SLIC were the most influential, followed by textural and morphological traits. Integrating diverse features substantially reduced error rates from 8% (SLIC-only) to 1% with multi-feature integration, demonstrating the synergistic benefits of combining segmentation, feature fusion, and ensemble learning. This research strongly suggests the benefits of employing such a robust hybrid approach, combining the strengths of segmentation and pixel-based methods and advanced machine learning classifiers to achieve scalable and high-resolution crop mapping in complex agricultural environments. In conclusion, these findings provide actionable strategies for mapping and monitoring crops in smallholder systems, where technical and financial constraints limit multispectral adoption. By prioritising accessible RGB sensors, simple algorithms, and phenological timing, this work supports scalable precision agriculture in developing countries, ultimately aiding food security and sustainable land management. The research highlights the importance of considering the phenological stage and leveraging specific spectral regions, as demonstrated by the hyperspectral analysis. It also underscores the significant role of morphological features derivable from UAS-based RGB imagery for effective crop differentiation. Finally, the proposed hybrid segmentation-classification approach showcases the potential for integrating diverse features and advanced machine learning algorithms for achieving high accuracy in heterogeneous landscapes. The collective insights from these investigations contribute significantly to precision agriculture, offering valuable methodologies and findings that can be further developed and implemented for improved crop monitoring and management, especially in resource-constrained agricultural systems prevalent in regions like sub-Saharan Africa (SSA) and similar environments worldwide. Future research should focus on translating these ground and UAS-based insights to satellite platforms, enabling broader regional scalability while maintaining accuracy in complex cropping systems. Further research could also focus on integrating these diverse approaches, exploring the transferability of these techniques across different crop types and geographical locations, and developing user-friendly tools for practical implementation by agricultural stakeholders.Item The sardine run : investigating sardine and predator distribution in relation to environmental conditions using GIS and remotely sensed products.(2009) O'Donoghue, Sean Henry.; Peddemors, Victor Marten.The sardine run is a spectacular but poorly understood natural phenomenon. This research aims to broaden scientific knowledge pertaining to sardine, Sardinops sagax, distribution, both in relation to their predators and environmental conditions. Sardine distribution was closely related to sea temperature. Sardines were sighted every year along the Lower Wild Coast, where continental shelf conditions were cooled by the Port Alfred upwelling cell. To the north of Mbashe River, shelf conditions were dominated by the warm Agulhas Current, and sardine distribution varied annually in close relation with sea temperature conditions. Along this coastline sardine abundance always peaked between Waterfall Bluff and Port St Johns with favourable conditions caused by the westward inflection of the coastline and the shelf bathymetry. Topographically-induced upwelling was concluded to be the cause of cooler sea temperatures and elevated chl a concentrations. Although chl a concentration appeared to be associated with east coast sardine distribution, the uncertainty with regards data accuracy hindered their usefulness as a predictor of suitable biological conditions for sardine. Sardine northward movement along the KZN coastline was impeded adjacent to the Durban Eddy, where they were forced shorewards by the warm conditions. This coincided with the peak in beach seine catches. The Cape Gannet, Morus capensis, was very closely associated with sardine along the entire east coast. Their abundance declined substantially adjacent to the Durban Eddy. Prevailing atmospheric conditions affected gannet behaviour: they travelled more frequently during strong alongshore winds and foraged more upon cessation of such winds. Gannets were closely associated with feeding dolphins at both coarse and fine scales, and responded to changes in dolphin behaviour. Common dolphin, Delphinus capensis, abundance and group size peaked between Waterfall Bluff and Port St Johns. Along this stretch of coastline they travelled more slowly, and in pods more perpendicular to the bathymetry of the region. Bottlenose dolphin, Tursiops aduncus, abundance increased during the sardine run with the influx of a migrant stock which reached the KZN Mid South Coast. Humpback Whale, Megaptera novaeangliae, and sardine distributions did not appear to be related.Item A study of some of the inter-relationships between maize and the seed storage fungi as typified by Aspergillus flavus var. columnaris.(1990) Mycock, David John.; Berjak, Patricia.The seed storage fungi (xerotolerant) species of the genera Aspergillus and Penicillium} are renowned for their devastating effects on stored grain and grain products. In view of the fact that most of these fungi Iiberate toxins which can be harmful to both man and his livestock this problem is becoming increasingly relevant, particularly in developing countries. The seed storage fungi are said to be saprophytes and opportunistic invaders of dead or naturally dried organic matter, and as such no direct host-pathogen relationship has been ascribed to them. This dissertation reports aspects of an investigation into the modes/pathways utilised by these fungi in their infection of maize caryopses (seeds) and plants. The work involved studies on: the effects of protracted storage on maize seeds; the morphology of storage fungi; extra-cellular enzymes of storage fungi; the pathways utiIised by the storage fungi in invasion of seed tissues; and the effects of the storage fungi on the seeds. Correlations have been made on a species basis between the extent of seed deterioration and fungal aggressiveness. The results of these investigations indicated that apart from affecting seed vigour and viability, these fungi can also affect plant vigour. This latter aspect was further investigated to determine whether a seed storage fungus could infect germinating maize seeds, and remain an internal contaminant of the tissues during plant growth and development. These latter studies revealed that Aspergillus flavus var. columnaris is capable of systemic transmission from one seed generation to the next. This hitherto unrecognised phenomenon apart from indicating that the fungal species is in fact a biotroph as well as a saprophyte, also has implications In control measures.Item The effects of elephant and mesoherbivores on woody vegetation.(2011) Lagendijk, Daisy Diana Georgette.; Slotow, Robert Hugh.; Page, Bruce Richard.Herbivores are important drivers and have a longstanding history in shaping our terrestrial environments. However, during the past decades, changes in woody vegetation in savanna and forest systems have been observed in southern Africa. Subsequently, concerns have been raised about the loss of (tall) trees in areas with elephant. The relative effects of browsing herbivores on vegetation and the potential browsing interaction with other herbivore species remain unclear and were examined using vegetation transects and exclosure experiments in savanna woodland and Sand Forest. Rainfall, fire and elephant were important savanna determinants. Especially rainfall positively affected woody densities, which were negatively affected by a longer exposure time to elephant, but not to elephant densities itself. In general, within South Africa’s savannas, tree height classes were absent from the population demography. Different height classes were likely to be impacted by different drivers. For example, seedling and sapling densities were greater with longer fire return periods and increased rainfall. The Sand Forest exclosure experiments showed that forest regeneration was impacted by nyala and both elephant and nyala, as the absence of both species increased tree densities. Both species combined, and individually, also affected tree species assemblages. In contrast, short term elephant access to a savanna area did not affect tree densities or species assemblages. In both savanna and Sand Forest elephant displaced mesoherbivores, and in Sand Forest both elephant and mesoherbivores displaced their smaller counterparts. The presence of competitive displacement also affected recruitment (i.e. seedlings and/or saplings) of woody vegetation both in Sand Forest and savanna. Thus, elephant and mesoherbivores exert direct and indirect (i.e. competitive displacement providing a window for recruitment) impact on vegetation. Active management of the herbivore species assemblage affects both vegetation and other herbivores, which effects potentially cascade into lower trophic levels, jeopardising biodiversity and ecosystem processes. Therefore, the full herbivore assemblage present and their combined and individual browsing effects need to be considered when setting management goals to conserve habitats and biodiversity across all trophic levels. In addition some contrasting results between Sand Forest and savanna emphasise the need for caution when extrapolating results from different areas and ecosystems.Item Systematic revision of the golden mole genera : Amblysomus, Chlorotalpa and Calcochloris (Insectivora : Chrysochloromorpha ; Chrysochloridae)(1995) Bronner, Gary N.; Meester, Jurgens Anthonie Jansen.; Rautenbach, Ignatius Lourens.; Cooke, John Anthony.; Willan, Kenneth Brian Ronald.Patterns of variation in hyoid morphology, chromosomal properties and craniodental characteristics among ten chrysochlorid species from South Africa were studied to clarify generic relationships among taxa assigned variably to Amblysomus, Chlorotalpa and Calcochloris by previous authors. Intra-specific variation in hyoid morphology was negligible, but inter-specific differences were marked. Similarly, intra-specific karyotypic variation was negligible, except in A. hottentotus, which displayed three cytotypes. These data supported the recognition of Chlorotalpa, Calcochloris and Neamblysomus as taxa distinct from Amblysomus. Only one (presence/absence of M3) of the seven dental traits used by previous authors was consistent enough within species to be taxonomically useful in this work. Dental variability within species appeared to arise from the morphological differences between deciduous and permanent teeth, which may occur together in the same toothrow. Intra-specific craniometric variation in most species involved pronounced sexual size dimorphism, but negligible age-related variation. In the more widespread species, patterns of geographic variation were dominated by divergence in overall size, although subtle differences in cranial shape were also evident. Multivariate analyses confirmed the validity of subspecies in Chlorotalpa sclateri and Calcochloris obtusirostris, and showed that A. hottentotus (as traditionally recognized) includes: four cryptic species; five subspecies (including A. h. iris); and several populations that should be relegated to A. corriae. Inter-specific morphometric variation was dominated by overall size. The species fell into two size groups, and eight phena that differed mainly in skull width, palatal shape, rostrum breadth and claw size. Inter-specific relationships suggested by phenetic analyses of metric and mixed-mode data were, however, incongruent owing to discordance between different data suites. Evolutionary relationships inferred by integrating data suites, using either equal or differential weights, indicated that a strong phylogenetic signal was present in the data. Phylogenetic analyses showed that the differentially weighted treatment was more consilient with character-state distributions. A phylogram based on the differential-weights cladogram was used to derive a revised phylogenetic classification for the Chrysochloridae. Unlike previous treatments, this classification affords Carpitalpa and Neamblysomus generic rank, and assigns C. leucorhina from equatorial Africa to Calcochloris, rather than to Chlorotalpa.