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Research article - Peer-reviewed, 2018

Classification of Maize in Complex Smallholder Farming Systems Using UAV Imagery

Hall, Ola; Dahlin, Sigrun; Marstorp, Håkan; Archila Bustos, Maria Francisca; Öborn, Ingrid; Jirström, Magnus

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.

Keywords

UAV; remote sensing; maize; OBIA; Ghana

Published in

Drones
2018, volume: 2, number: 3, article number: 22

Authors' information

Hall, Ola
Lund University
Swedish University of Agricultural Sciences, Department of Soil and Environment
Marstorp, Håkan
Swedish University of Agricultural Sciences, Department of Soil and Environment
Archila Bustos, Maria Francisca
Lund University
Swedish University of Agricultural Sciences, Department of Crop Production Ecology
World Agroforestry Centre (ICRAF)
Center for International Forestry Research (CIFOR)
Jirström, Magnus
Lund University

UKÄ Subject classification

Agricultural Science
Remote Sensing

Publication Identifiers

DOI: https://doi.org/10.3390/drones2030022

URI (permanent link to this page)

https://res.slu.se/id/publ/98500