Abstract
Diseases affecting agricultural sectors are often closely related to weather conditions and crop management. In this regard, different researches have focused on identifying patterns that lead to the incidence of these diseases. This research was carried out in order to detect favorable conditions for rust in coffee trees (hemileia vastatrix) based on a graph representation of the Agroclimatic information of the crops. Furthermore, we adapted 4 error-correcting graph pattern matching algorithms, classified taking into account the precision and the execution time, in order to find a similarity percentage between current conditions of a coffee crop and the graph patterns that describe coffee rust infection rates.
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Acknowledgments
The authors are grateful to the Telematics Engineering Group (GIT) of the university of Cauca, the Colombian Administrative Department of Science, Technology and Innovation (Colciencias), AgroCloud project of The Interinstitutional Network of Climate Change and Food Security of Colombia (RICCLISA) for supporting this research and Supracafé for providing the dataset used.
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Lozada, G., Valencia, G., Lasso, E., Corrales, J.C. (2018). Coffee Rust Detection Based on a Graph Similarity Approach. In: Angelov, P., Iglesias, J., Corrales, J. (eds) Advances in Information and Communication Technologies for Adapting Agriculture to Climate Change. AACC'17 2017. Advances in Intelligent Systems and Computing, vol 687. Springer, Cham. https://doi.org/10.1007/978-3-319-70187-5_7
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DOI: https://doi.org/10.1007/978-3-319-70187-5_7
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