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Drones 2018, 2(3), 22; https://doi.org/10.3390/drones2030022

Classification of Maize in Complex Smallholder Farming Systems Using UAV Imagery

1
Department of Human Geography, Lund University, 223 00 Lund, Sweden
2
Department of Soil and Environment, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
3
Department of Crop Production Ecology, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
4
World Agroforestry Centre, P.O. Box 161, Bogor 16001,West Java, Indonesia
5
Center for International Forestry Research, Sindang Barang, Bogor 16115, Indonesia
*
Author to whom correspondence should be addressed.
Received: 5 June 2018 / Revised: 19 June 2018 / Accepted: 19 June 2018 / Published: 22 June 2018
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Abstract

Yield estimates and yield gap analysis are important for identifying poor agricultural productivity. Remote sensing holds great promise for measuring yield and thus determining yield gaps. Farming systems in sub-Saharan Africa (SSA) are commonly characterized by small field size, intercropping, different crop species with similar phenologies, and sometimes high cloud frequency during the growing season, all of which pose real challenges to remote sensing. Here, an unmanned aerial vehicle (UAV) system based on a quadcopter equipped with two consumer-grade cameras was used for the delineation and classification of maize plants on smallholder farms in Ghana. Object-oriented image classification methods were applied to the imagery, combined with measures of image texture and intensity, hue, and saturation (IHS), in order to achieve delineation. It was found that the inclusion of a near-infrared (NIR) channel and red–green–blue (RGB) spectra, in combination with texture or IHS, increased the classification accuracy for both single and mosaic images to above 94%. Thus, the system proved suitable for delineating and classifying maize using RGB and NIR imagery and calculating the vegetation fraction, an important parameter in producing yield estimates for heterogeneous smallholder farming systems. View Full-Text
Keywords: UAV; remote sensing; maize; OBIA; Ghana UAV; remote sensing; maize; OBIA; Ghana
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Hall, O.; Dahlin, S.; Marstorp, H.; Archila Bustos, M.F.; Öborn, I.; Jirström, M. Classification of Maize in Complex Smallholder Farming Systems Using UAV Imagery. Drones 2018, 2, 22.

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