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Examining the potential of open source remote sensing for building effective decision support systems for precision agriculture in resource-poor settings

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Abstract

Precision agriculture (PA) has become increasingly important to farmers particularly in resource-poor and risk-prone settings in the developing world. However, due to cost and technical constraints, deploying PA infrastructure as decision support systems (DSSs) in smallholder farming settings is often hindered. This paper draws on freely available satellite data (Sentinel-2A) and software (SNAP Toolbox), within the framework of open source remote sensing (OSRS) to demonstrate the potential of monitoring crop health and development towards building an effective DSS to inform farm management and resource allocation decision making using the Tono Irrigation Scheme—a resource-poor rural irrigation system in Ghana, as a case study. We find that vegetation index algorithms in SNAP Toolbox can accurately identify biophysical and growth conditions of crops including chlorophyll content, nitrogen status, pest and disease infestation, and water requirements. Despite the potential inherent in this novel cost-effective OSRS-based monitoring system, basic training of scheme managers and extension officers is required to enable them interpret output from OSRS analysis. Given the potential to reduce costs, improve allocation of scarce resources and increase yields, it is worth implementing OSRS as a DSS for smallholder farmers in other resource-poor settings.

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Notes

  1. The difference between crop yields observed at any given location and the crop’s potential yield at the same location given current agricultural practices and technologies (Foley et al. 2011:339).

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  1. Department of Geography, University of Western Ontario, 1151 Richmond Street, London, ON, N6A 5C2, Canada

    Daniel Kpienbaareh, Moses Kansanga & Isaac Luginaah

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  1. Daniel Kpienbaareh
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Correspondence to Daniel Kpienbaareh.

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The study obtained ethical approval from the Council for Industrial and Scientific Research, Ghana. All procedures involving the conduct of interviews with the participants were observed and participants willingly took part in the study.

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Kpienbaareh, D., Kansanga, M. & Luginaah, I. Examining the potential of open source remote sensing for building effective decision support systems for precision agriculture in resource-poor settings. GeoJournal 84, 1481–1497 (2019). https://doi.org/10.1007/s10708-018-9932-x

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