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Article

Genetic Programming Approach for the Detection of Mistletoe Based on UAV Multispectral Imagery in the Conservation Area of Mexico City

1
Centro de Investigación en Ciencias de Información Geoespacial (CentroGeo), Ciudad de México 14240, Mexico
2
CONACYT—CentroGeo, Aguascalientes 20313, Mexico
3
CONACYT—CentroGeo, Yucatán 97302, Mexico
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Michael Sprintsin
Remote Sens. 2022, 14(3), 801; https://doi.org/10.3390/rs14030801
Received: 1 December 2021 / Revised: 26 January 2022 / Accepted: 3 February 2022 / Published: 8 February 2022
(This article belongs to the Special Issue Detecting Anomalies and Tracking Biodiversity for Forest Monitoring)
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 96% and a value of fitness function based on weighted Cohen’s Kappa (kw) equal to 0.45 in the test data set. Additionally, our method’s 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. View Full-Text
Keywords: evolutionary computation; image detection; forest pest; supervised learning; vegetation index; computer vision; change detection evolutionary computation; image detection; forest pest; supervised learning; vegetation index; computer vision; change detection
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MDPI and ACS Style

Mejia-Zuluaga, P.A.; Dozal, L.; Valdiviezo-N., J.C. Genetic Programming Approach for the Detection of Mistletoe Based on UAV Multispectral Imagery in the Conservation Area of Mexico City. Remote Sens. 2022, 14, 801. https://doi.org/10.3390/rs14030801

AMA Style

Mejia-Zuluaga PA, Dozal L, Valdiviezo-N. JC. Genetic Programming Approach for the Detection of Mistletoe Based on UAV Multispectral Imagery in the Conservation Area of Mexico City. Remote Sensing. 2022; 14(3):801. https://doi.org/10.3390/rs14030801

Chicago/Turabian Style

Mejia-Zuluaga, Paola Andrea, León Dozal, and Juan C. Valdiviezo-N. 2022. "Genetic Programming Approach for the Detection of Mistletoe Based on UAV Multispectral Imagery in the Conservation Area of Mexico City" Remote Sensing 14, no. 3: 801. https://doi.org/10.3390/rs14030801

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