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Object-Oriented Classification of Maize from UAV Imagery in African mixed smallholder farming systems

Hall, Ola and Dahlin, Sigrun and Marstorp, Håkan and Archila Bustos, Maria Francisca and Öborn, Ingrid and Jirström, Magnus (2018). Object-Oriented Classification of Maize from UAV Imagery in African mixed smallholder farming systems. Drones. 2, 1-8
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Official URL: http://dx.doi.org/10.3390/drones2030022

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.

Authors/Creators:Hall, Ola and Dahlin, Sigrun and Marstorp, Håkan and Archila Bustos, Maria Francisca and Öborn, Ingrid and Jirström, Magnus
Title:Object-Oriented Classification of Maize from UAV Imagery in African mixed smallholder farming systems
Series/Journal:Drones (2504-446X)
Year of publishing :2018
Volume:2
Page range:1-8
Number of Pages:8
Publisher:MDPI
ISSN:2504-446X
Language:English
Publication Type:Journal article
Refereed:Yes
Article category:Scientific peer reviewed
Version:Published version
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Agricultural Science
(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing
Keywords:UAV, remote sensing, maize, OBIA, Ghana
URN:NBN:urn:nbn:se:slu:epsilon-e-5239
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-e-5239
Additional ID:
Type of IDID
DOI10.3390/drones2030022
ID Code:15932
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
Department:(NL, NJ) > Dept. of Crop Production Ecology
(NL, NJ) > Dept. of Soil and Environment
(S) > Dept. of Soil and Environment
Deposited By: SLUpub Connector
Deposited On:08 Mar 2019 14:17
Metadata Last Modified:08 Mar 2019 14:17

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