Discriminating Robusta coffee (Coffea canephora) cropping systems using leaf-level hyperspectral data
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 diver...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/173235 |
| _version_ | 1855526367823659008 |
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| author | Kebede, G. Mudereri, B.T. Abdel-Rahman, E.M. Mutanga, O. Landmann, T. Odindi, J. Motisi, N. Pinard, F. Tonnang, H.E.Z. |
| author_browse | Abdel-Rahman, E.M. Kebede, G. Landmann, T. Motisi, N. Mudereri, B.T. Mutanga, O. Odindi, J. Pinard, F. Tonnang, H.E.Z. |
| author_facet | Kebede, G. Mudereri, B.T. Abdel-Rahman, E.M. Mutanga, O. Landmann, T. Odindi, J. Motisi, N. Pinard, F. Tonnang, H.E.Z. |
| author_sort | Kebede, G. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace173235 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| record_format | dspace |
| spelling | CGSpace1732352025-12-08T10:11:39Z Discriminating Robusta coffee (Coffea canephora) cropping systems using leaf-level hyperspectral data Kebede, G. Mudereri, B.T. Abdel-Rahman, E.M. Mutanga, O. Landmann, T. Odindi, J. Motisi, N. Pinard, F. Tonnang, H.E.Z. machine learning agroforestry cropping systems 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. 2024-10-04 2025-02-19T20:13:57Z 2025-02-19T20:13:57Z Journal Article https://hdl.handle.net/10568/173235 en Limited Access Kebede, G.; Mudereri, B.T.; Abdel-Rahman, E.M.; Mutanga, O.; Landmann, T.; Odindi, J.; Motisi, N.; Pinard, F.; Tonnang, H.E.Z. 2024. Discriminating Robusta coffee (Coffea canephora) cropping systems using leaf-level hyperspectral data. Journal of Applied Remote Sensing. ISSN 1931-3195. 18(04). https://doi.org/10.1117/1.jrs.18.044503 |
| spellingShingle | machine learning agroforestry cropping systems Kebede, G. Mudereri, B.T. Abdel-Rahman, E.M. Mutanga, O. Landmann, T. Odindi, J. Motisi, N. Pinard, F. Tonnang, H.E.Z. Discriminating Robusta coffee (Coffea canephora) cropping systems using leaf-level hyperspectral data |
| title | Discriminating Robusta coffee (Coffea canephora) cropping systems using leaf-level hyperspectral data |
| title_full | Discriminating Robusta coffee (Coffea canephora) cropping systems using leaf-level hyperspectral data |
| title_fullStr | Discriminating Robusta coffee (Coffea canephora) cropping systems using leaf-level hyperspectral data |
| title_full_unstemmed | Discriminating Robusta coffee (Coffea canephora) cropping systems using leaf-level hyperspectral data |
| title_short | Discriminating Robusta coffee (Coffea canephora) cropping systems using leaf-level hyperspectral data |
| title_sort | discriminating robusta coffee coffea canephora cropping systems using leaf level hyperspectral data |
| topic | machine learning agroforestry cropping systems |
| url | https://hdl.handle.net/10568/173235 |
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