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Detection of Phoradendron Velutinum Implementing Genetic Programming in Multispectral Aerial Images in Mexico City

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Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

This research implements Genetic Programming to design a spectral index that allows the automated detection of the species Phoradendron Velutinum because it is a pest that leads to the detriment of forest health causing serious damage to the host trees. Employing multispectral aerial images taken in the field, pre-processed and selected for the creation of a set of masks with the presence of the pest, together with the use of different terminals and functions, it was possible to obtain an algorithm capable of classifying mistletoe with 96% overall accuracy and a fitness value (Weighted Cohen’s Kappa = 0.45) on the test data set. Additionally, a comparison was made with the Structure Intensive Pigment Index 2—SIPI2 for the detection of P. velutinum, the results show that SIPI2 does not allow the correct identification of this particular pest.

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Acknowledgements

The authors would like to thank Ing. Israel Jimenez and the Commission for Natural Resources and Rural Development—CORENA for the support and contribution of their experience and accompaniment in the field for the detection of P. velutinum in the conservation soil of the City of Mexico.

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Correspondence to Paola Andrea Mejia-Zuluaga .

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Mejia-Zuluaga, P.A., Dozal-García, L.F., Valdiviezo-Navarro, J.C. (2022). Detection of Phoradendron Velutinum Implementing Genetic Programming in Multispectral Aerial Images in Mexico City. In: Tapia-McClung, R., Sánchez-Siordia, O., González-Zuccolotto, K., Carlos-Martínez, H. (eds) Advances in Geospatial Data Science. iGISc 2021. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-030-98096-2_9

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