A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes
- Published
- Accepted
- Subject Areas
- Agricultural Science, Computational Science, Environmental Sciences, Human-Computer Interaction, Spatial and Geographic Information Science
- Keywords
- Sub-Saharan Africa, agriculture, machine learning, computer vision, land cover
- Copyright
- © 2015 Debats et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes. PeerJ PrePrints 3:e1367v1 https://doi.org/10.7287/peerj.preprints.1367v1
Abstract
Smallholder farms dominate in many parts of the world, particularly Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural land cover. Using a variety of sites in South Africa, we present a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. We achieved similar high performance across agricultural types, including the spectrally indistinct smallholder fields as well as the more easily distinguishable commercial fields, and demonstrated the ability to generalize performance across large geographic areas. In sensitivity analyses, we determined multi-temporal information provided greater gains in accuracy than multi-spectral information.
Author Comment
This manuscript has been submitted to another journal for review.