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Article

UAV-Based Mapping of Banana Land Area for Village-Level Decision-Support in Rwanda

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International Institute of Tropical Agriculture (IITA), KG 563 ST, Kigali P.O. Box 1269, Rwanda
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Leibniz Institute of Agricultural Development in Transition Economies (IAMO), 06120 Halle (Saale), Germany
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Knowledge, Technology and Innovation Group, Wageningen University, P.O. Box 8130, 6700 EW Wageningen, The Netherlands
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Author to whom correspondence should be addressed.
Academic Editor: Wataru Takeuchi
Remote Sens. 2021, 13(24), 4985; https://doi.org/10.3390/rs13244985
Received: 23 September 2021 / Revised: 5 November 2021 / Accepted: 3 December 2021 / Published: 8 December 2021
Crop monitoring is crucial to understand crop production changes, agronomic practice decision-support, pests/diseases mitigation, and developing climate change adaptation strategies. Banana, an important staple food and cash crop in East Africa, is threatened by Banana Xanthomonas Wilt (BXW) disease. Yet, there is no up-to-date information about the spatial distribution and extent of banana lands, especially in Rwanda, where banana plays a key role in food security and livelihood. Therefore, delineation of banana-cultivated lands is important to prioritize resource allocation for optimal productivity. We mapped the spatial extent of smallholder banana farmlands by acquiring and processing high-resolution (25 cm/px) multispectral unmanned aerial vehicles (UAV) imageries, across four villages in Rwanda. Georeferenced ground-truth data on different land cover classes were combined with reflectance data and vegetation indices (NDVI, GNDVI, and EVI2) and compared using pixel-based supervised multi-classifiers (support vector models-SVM, classification and regression trees-CART, and random forest–RF), based on varying ground-truth data richness. Results show that RF consistently outperformed other classifiers regardless of data richness, with overall accuracy above 95%, producer’s/user’s accuracies above 92%, and kappa coefficient above 0.94. Estimated banana farmland areal coverage provides concrete baseline for extension-delivery efforts in terms of targeting banana farmers relative to their scale of production, and highlights opportunity to combine UAV-derived data with machine-learning methods for rapid landcover classification. View Full-Text
Keywords: Rwanda; banana; machine learning; UAV; remote sensing; land cover mapping; precision agriculture; food security; BXW Rwanda; banana; machine learning; UAV; remote sensing; land cover mapping; precision agriculture; food security; BXW
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MDPI and ACS Style

Kilwenge, R.; Adewopo, J.; Sun, Z.; Schut, M. UAV-Based Mapping of Banana Land Area for Village-Level Decision-Support in Rwanda. Remote Sens. 2021, 13, 4985. https://doi.org/10.3390/rs13244985

AMA Style

Kilwenge R, Adewopo J, Sun Z, Schut M. UAV-Based Mapping of Banana Land Area for Village-Level Decision-Support in Rwanda. Remote Sensing. 2021; 13(24):4985. https://doi.org/10.3390/rs13244985

Chicago/Turabian Style

Kilwenge, Regina, Julius Adewopo, Zhanli Sun, and Marc Schut. 2021. "UAV-Based Mapping of Banana Land Area for Village-Level Decision-Support in Rwanda" Remote Sensing 13, no. 24: 4985. https://doi.org/10.3390/rs13244985

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