“Sistema de detección de ocotea heterochroma basado en técnicas de aprendizaje profundo en el bosque tropical murocomba”

The research project focused on making up for the lack of technological tools that allow the identification and storage of data on the species ocotea heterochroma belonging to the Lauraceae family through images, as well as the need to understand its vegetation to help preserve the Murocomba Tropical Forest. The methodology implemented was quantitative and qualitative. Through projective techniques using aerial photographs obtained through a drone. According to the previous literature studied and the criteria issued by forestry specialists, the specific study was oriented to the species ocotea heterochroma "Canelo Blanco" due to its medicinal properties and for being a species considered in danger of extinction, applying the deep learning model YOLOv5 for the automatic detection of this species, in addition, a mobile application was developed that allowed accessibility to the stored information and an API server for data collection and feedback. The project contributed to improve the understanding of tree diversity and extent, fostering conservation based on data visualization, promoting sustainable and environmentally friendly ecotourism practices, thus supporting its efficient management and regional development using artificial intelligence.

• Autore principale: Samaniego Barrezueta, Luis Adonis (author)
• Natura: bachelorThesis
• Lingua: spa
• Pubblicazione: 2023
• Soggetti: Inteligencia artificial; Algoritmo neuronal; Aprendizaje automático; Ecoturismo sostenible; Arboles en peligro de extinción; Conservación de la biodiversidad
• Accesso online: https://repositorio.uteq.edu.ec/handle/43000/7126