Mr. Elfatih M. Abdel-Rahman Profile

Elfatih M. Abdel-Rahman

Associate Professor

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Author

Publications (4)

SPIE Journal Paper | 4 October 2024

Discriminating Robusta coffee (Coffea canephora) cropping systems using leaf-level hyperspectral data

Getachew Kebede Aragaw, Bester Tawona Mudereri, Elfatih Abdel-Rahman, Onisimo Mutanga, Tobias Landmann, John Odindi, Natacha Motisi, Fabrice Pinard, Henri E. Tonnang

JARS, Vol. 18, Issue 04, 044503, (October 2024) https://doi.org/10.1117/12.10.1117/1.JRS.18.044503

KEYWORDS: Sun, Vegetation, Agriculture, Remote sensing, Reflectivity, Data modeling, Feature selection, Random forests, Chlorophyll, Shrinkage

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The coffee agro-ecosystems are increasingly being transformed into small-scale coffee-growing agricultural systems. In this context, the challenge of accurately classifying coffee cropping systems (CSs) becomes more significant, particularly in regions such as Uganda where dense vegetation and diverse topography complicate traditional land surveys. We harness the capabilities of remote sensing to provide hyperspectral data crucial for distinguishing between various coffee CSs and other land covers. Specifically, we focus on the spectral analysis of three types of Robusta coffee CSs—those integrating agroforestry, those combined with banana cultivation, and those in full sun exposure. Using in situ hyperspectral measurements captured by the FieldSpec 2™ spectroradiometer across the 325 to 1075 nm range of the electromagnetic spectrum, we aimed to (1) analyze the unique spectral properties and behaviors of these Robusta coffee CSs and (2) effectively discriminate among them using advanced hyperspectral datasets alongside the machine learning (ML) classification algorithms. The key to this process was the use of narrow spectral bands (NSBs) and various narrow-band vegetation indices (VIs), serving as predictor variables. A selection of critical variables (NSB = 9 and VIs = 8) was identified through the guided regularized random forest (RF) technique and then applied to four ML algorithms—RF, stochastic gradient boosting (GB), linear discriminant analysis, and support vector machine for classification experiments. The findings indicated high discrimination accuracy, with the RF and GB algorithms achieving overall accuracies of 93% and 90.5%, respectively, when using the selected VIs, and 87.3% (RF) and 83% (GB) when applying the chosen NBSs. These results underline the efficacy of integrating hyperspectral datasets and ML algorithms in reliably categorizing Robusta coffee CSs, a crucial step toward enhancing sustainable coffee cultivation practices.

SPIE Journal Paper | 1 December 2020

Landscape fragmentation in coffee agroecological subzones in central Kenya: a multiscale remote sensing approach

Gladys Mosomtai, John Odindi, Elfatih Abdel-Rahman, Régis Babin, Pinard Fabrice, Onisimo Mutanga, Henri E. Tonnang, Guillaume David, Tobias Landmann

JARS, Vol. 14, Issue 04, 044513, (December 2020) https://doi.org/10.1117/12.10.1117/1.JRS.14.044513

KEYWORDS: Picosecond phenomena, Satellites, Earth observing sensors, Data modeling, Vegetation, Landsat, Satellite imaging, Near infrared, Agriculture, Spatial resolution

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SPIE Journal Paper | 20 May 2015

Varietal discrimination of common dry bean (Phaseolus vulgaris L.) grown under different watering regimes using multitemporal hyperspectral data

Perushan Rajah, John Odindi, Elfatih Abdel-Rahman, Onisimo Mutanga, Albert Modi

JARS, Vol. 9, Issue 01, 096050, (May 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.096050

KEYWORDS: Data modeling, Reflectivity, Visible radiation, Shortwaves, Short wave infrared radiation, Infrared radiation, Agriculture, Statistical analysis, Electromagnetism, Visualization

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Proceedings Article | 2 October 2008 Paper

Imaging spectroscopy for estimating sugarcane leaf nitrogen concentration

Elfatih Abdel-Rahman, Fethi Ahmed, Maurits van den Berg

Proceedings Volume 7104, 71040V (2008) https://doi.org/10.1117/12.800221

KEYWORDS: Nitrogen, Reflectivity, Vegetation, Chemical analysis, Imaging spectroscopy, Spectroscopy, Electromagnetism, Statistical analysis, Infrared radiation, Mid-IR

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