Authors:
Jonathan Loor
1
;
Ariana Jiménez
1
;
Juan Aguirre
2
;
Grace Rodríguez
3
;
Iván Reyes
2
;
Paulina Vizcaino-Imacaña
2
and
Manuel Morocho-Cayamcela
1
;
2
Affiliations:
1
Yachay Tech University, School of Mathematical and Computational Sciences, DeepARC Research Group, Hda. San José s/n y Proyecto Yachay, Urcuquí, 100119, Ecuador
;
2
Universidad Internacional del Ecuador, Faculty of Technical Sciences, School of Computer Science, Quito, 170411, Ecuador
;
3
Pontificia Universidad Cat ólica del Ecuador, Faculty of Exact and Natural Sciences Sciences, Biology, Quito, 170525, Ecuador
Keyword(s):
Pest Management, Invasive Species Detection, Biodiversity Conservation, Galápagos Islands, Artificial Intelligence, Transformer time-series, Mobile Application Development.
Abstract:
The Galápagos Archipelago are confronting a significant threat from invasive species, notably L. fulica, which disrupts the delicate balance of their natural ecosystem. An innovative solution is proposed, employing mobile application technology and artificial intelligence (AI) to streamline the collection, analysis, and prediction of L. fulica movements. The mobile application facilitates efficient recording of L. fulica sightings by field teams, including Global Positioning System (GPS) coordinates, type, condition, and quantity. Data collected is transmitted to a cloud-based server for storage and analysis, where machine learning algorithms process time-series data to generate predictive models of L. fulica movement patterns. Results underscore the effectiveness of AI in enhancing the efficiency and accuracy of Giant African Snail (GAS) detection and movement estimation, facilitating informed decision-making by administrators and managers. By safeguarding the native flora and fauna
of the archipelago, this solution represents a significant stride towards mitigating the impact of invasive species and preserving the unique biodiversity of the Galapagos Islands.
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