Genetic Programming Approach for the Detection of Mistletoe Based on UAV Multispectral Imagery in the Conservation Area of Mexico City
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- @Article{mejia-zuluaga:2022:Remote_Sensing,
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author = "Paola Andrea Mejia-Zuluaga and Leon Dozal and
Juan C. Valdiviezo-N.",
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title = "Genetic Programming Approach for the Detection of
Mistletoe Based on {UAV} Multispectral Imagery in the
Conservation Area of Mexico City",
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journal = "Remote Sensing",
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year = "2022",
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volume = "14",
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number = "3",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2072-4292",
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URL = "https://www.mdpi.com/2072-4292/14/3/801",
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DOI = "doi:10.3390/rs14030801",
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abstract = "The mistletoe Phoradendron velutinum (P. velutinum) is
a pest that spreads rapidly and uncontrollably in
Mexican forests, becoming a serious problem since it is
a cause of the decline of 23.3 million hectares of
conifers and broadleaves in the country. The lack of
adequate phytosanitary control has negative social,
economic, and environmental impacts. However, pest
management is a challenging task due to the difficulty
of early detection for proper control of mistletoe
infestations. Automating the detection of this pest is
important due to its rapid spread and the high costs of
field identification tasks. This paper presents a
Genetic Programming (GP) approach for the automatic
design of an algorithm to detect mistletoe using
multispectral aerial images. Our study area is located
in a conservation area of Mexico City, in the San
Bartolo Ameyalco community. Images of 148 hectares were
acquired by means of an Unmanned Aerial Vehicle (UAV)
carrying a sensor sensitive to the R, G, B, red edge,
and near-infrared bands, and with an average spatial
resolution of less than 10 cm per pixel. As a result,
it was possible to obtain an algorithm capable of
classifying mistletoe P. velutinum at its flowering
stage for the specific case of the study area in
conservation area with an Overall Accuracy (OA) of
96percent and a value of fitness function based on
weighted Cohens Kappa (kw) equal to 0.45 in the test
data set. Additionally, our methods performance was
compared with two traditional image classification
methods; in the first, a classical spectral index,
named Intensive Pigment Index of Structure 2 (SIPI2),
was considered for the detection of P. velutinum. The
second method considers the well-known Support Vector
Machine classification algorithm (SVM). We also compare
the accuracy of the best GP individual with two
additional indices obtained during the solution
analysis. According to our experimental results, our
GP-based algorithm outperforms the results obtained by
the aforementioned methods for the identification of P.
velutinum.",
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notes = "also known as \cite{rs14030801}",
- }
Genetic Programming entries for
Paola Andrea Mejia-Zuluaga
Leon Dozal
Juan C Valdiviezo-N
Citations