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

Kebede Getachew, Mudereri Bester Tawona, Abdel-Rahman Elfatih M., Mutanga Onisimo, Landmann Tobias, Odindi John, Motisi Natacha, Pinard Fabrice, Tonnang Henri E.Z.. 2024. Discriminating Robusta coffee (Coffea canephora) cropping systems using leaf-level hyperspectral data. Journal of Applied Remote Sensing, 18 (4), 17 p.

Url - jeu de données - Entrepôt autre : https://dmmg.icipe.org/dataportal/dataset/discriminating-robusta-coffee-coffea-canephora-cropping-systems

Résumé : 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.

Mots-clés Agrovoc : Coffea canephora, télédétection, Coffea, agroforesterie, anatomie végétale, Coffea arabica, analyse discriminante, apprentissage machine, système de culture

Mots-clés géographiques Agrovoc : Ouganda

Mots-clés libres : Variable selection, Uganda, In situ hyperspectral data, Machine Learning, Africa

Classification Agris : F08 - Systèmes et modes de culture
U30 - Méthodes de recherche

Champ stratégique Cirad : CTS 2 (2019-) - Transitions agroécologiques

Agences de financement européennes : European Commission

Agences de financement hors UE : Swedish International Development Cooperation Agency, Swiss Agency for Development and Cooperation, Australian Centre for International Agricultural Research, Norwegian Agency for Development Cooperation, Federal Democratic Republic of Ethiopia, Government of the Republic of Kenya, Centre de Coopération Internationale en Recherche Agronomique pour le Développement

Projets sur financement : (EU) Robusta coffee agroforestry to adapt and mitigate climate change in Uganda, (EU) Development Smart Innovation through Research in Agriculture

Auteurs et affiliations

• Kebede Getachew, ICIPE (KEN) - auteur correspondant
• Mudereri Bester Tawona, ICIPE (KEN)
• Abdel-Rahman Elfatih M., ICIPE (KEN)
• Mutanga Onisimo, University of KwaZulu-Natal (ZAF)
• Landmann Tobias, ICIPE (KEN)
• Odindi John, University of KwaZulu-Natal (ZAF)
• Motisi Natacha, CIRAD-BIOS-UMR PHIM (KEN) ORCID: 0000-0001-8313-6728
• Pinard Fabrice, CIRAD-BIOS-UMR PHIM (KEN)
• Tonnang Henri E.Z., ICIPE (KEN)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/610959/)

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