Browsing by Author "Dube, T"

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    Discrimination of tomato plants (solanum lycopersicum) grown under anaerobic baffled reactor effluent, nitrified urine concentrate and commercial hydroponic fertilizer regimes using multi-source satellite data
    (ISPRS, 2019) Sibanda, M; Mutanga, O; Dube, T
    We evaluate the detection and discriminative strength of three different satellite spectral settings, namely, HyspIRI, the forthcoming Landsat 9 and Sentinel 2-MSI, in mapping tomato (Solanum lycopersicum) plants grown under hydroponic system using humanexcreta derived materials (HEDM), namely, anaerobic baffled reactor (ABR) effluent and nitrified urine concentrate (NUC) and commercial hydroponic fertilizer mix (CHFM) as nutrient sources. Partial least squares – discriminant analysis (PLS-DA) and discriminant analysis (DA) were applied to discriminate tomatoes grown under these different nutrient sources. Results of this study showed that spectral settings of HyspIRI sensor can better discriminate tomatoes grown under different fertilizer regimes when compared to Landsat 9 OLI and Sentinel-2 MSI spectral configurations. For instance, based on DA algorithm, HyspIRI exhibited high overall accuracy of 0.99 and a kappa statistic of 0.99 whereas Landsat OLI and Sentinel-2 MSI exhibited over accuracies of 0.94 and 0.95 as well as kappa statistics of 0.79 and 0.85, respectively. Further, the performance of DA was significantly different (α = 0.05) from that of PLS-DA based on the MaNemar tests. Overall, the performance of HyspIRI, Landsat 9 OLI-2 and Sentinel-2 MSI data seem to bring new opportunities for crop monitoring at farm scale.
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    Modelling water utilization patterns in apple orchards with varying canopy sizes and different growth stages in semi-arid environments
    (Elsevier, 2021) Dube, T; Mobe, N.T; Dzikiti, S
    Accurate estimates of orchard evapotranspiration (ET) and its components are important for precise irrigation scheduling, irrigation system designs, and optimal on-farm water allocation particularly in water-limited environments. Direct measurements of ET remain costly, laborious and sometimes difficult to apply over heterogeneous surfaces such as crop fields. Therefore, accurate crop water-use models are required for on-farm precise water resources management. In this study, we adopted and improved the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model developed by Fisher et al 2008 to estimate crop water use across different apple plants. Specifically, the model was developed to quantify the partitioning of apple orchard water use into beneficial (tree transpiration) and non-beneficial water use (orchard floor evaporation) as influenced by tree canopy cover. Data were collected in twelve orchards spread across key apple producing regions in the Western Cape Province of South Africa over three growing seasons (2014/15, 2015/16, 2016/17). Model ET estimates were tested against ET data measured; using the eddy covariance method and transpiration measured based on sap flow monitoring techniques. The results showed that the original Fisher PT-JPL model performed poorly in ET estimation across all the orchards under study.
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    A two-step approach for detecting Striga in a complex agroecological system using Sentinel-2 data
    (Elsevier, 2021) Mudereri, B.T; Abdel-Rahman, Elfatih Mohamed; Dube, T
    Information on weed occurrence within croplands is vital but is often unavailable to support weeding practices and improve cropland productivity assessments. To date, few studies have been conducted to estimate and map weed abundances within agroecological systems from spaceborne images over wide-area landscapes, particularly for the genus Striga. Therefore, this study attempts to increase the detection capacity of Striga at subpixel size using spaceborne high-resolution imagery. In this study, a two-step classification approach was used to detect Striga (Striga hermonthica) weed occurrence within croplands in Rongo, Kenya. Firstly, multidate and multiyear Sentinel-2 (S2) data (2017 to 2018) were utilized to map cropland and non-cropland areas using the random forest algorithm within the Google Earth Engine. The non-cropland class was thereafter masked out from a single date S2 image of the 13th of December 2017. The remaining cropland area was then used in a subpixel multiple endmember spectral mixture analysis (MESMA) to detect Striga occurrence and infestation using endmembers (EMs) obtained from the in-situ hyperspectral data. The gathered in-situ hyperspectral data were resampled to the spectral waveband configurations of S2 and three representative EMs were inferred, namely: (1) Striga, (2) crop and other weeds, and (3) soil. Overall classification accuracies of 88% and 78% for the pixel-based cropland mapping and subpixel Striga detection were achieved, respectively.