Data-driven mobile application for native trees using ai-identification/ Joshua A. Castillon, Cristopher S. Chan, Alliana Hira G. Cordero, Ken Vaness M. Fundales, and Angel Junine P. Peñalosa.--

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Castillon, Joshua A [author]

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Material type: Text

TextPublication details: Manila: Technological University of the Philippines, 2025.Description: xxiii, 197pages: 29cmContent type:

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BTH QA 76.9 C37 2025

Dissertation note: College of Industrial Technology.-- Bachelor of engineering technology major in computer engineering technology: Technological University of the Philippines, 2025. Summary: The increasing loss of native tree species in urban environments, especially in Metro Manila, underscores the urgent need for products that can provide new means to access tools to raise awareness and identify and protect local flora. This study describes the creation of a data-driven mobile application to identify five native trees from the Philippines - Narra, Kamagong, Talisay, Banaba, and Ipil - using artificial intelligence- based leaf image identification. Using the mobile application, users can accurately identify trees and receive information on each tree species, including scientific names, ecological preferences, and growth habits. The system was equipped with an intuitive interface that includes a searchable tree database, as well as a map function to showcase important areas in the Philippines with each tree species. Nevertheless, the identification model was specifically trained only using native trees in and around the Arroceros Forest Park region, thus limiting appropriate identification to only those species in that park. The AI identification model includes a deep learning convolutional neural networks (CNN) trained on a designated leaf image dataset that was curated, enhancing performance reliability. The application was tested against a number of devices and Android versions, and the results showed excellent functioning, responsive controls, and consistent behavior. Limitations were the limitation to five tree species, and the identification accuracy was only relevant to those in Arroceros. Testing was done against the software quality standards ISO/IEC 25010 and showed that the system’s accuracy, functionality, and usability were adequate. This project is the start of an initiative for urban biodiversity by providing an object-based and usable tool for native tree identification, as well as advocacy for tree conservation.

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Holdings
Item type	Current library	Shelving location	Call number	Copy number	Status	Date due	Barcode
Bachelor's Thesis CIT	TUP Manila Library	Thesis Section-2nd floor	BTH QA 76.9 C37 2025 (Browse shelf(Opens below))	c.1	Not for loan		BTH0006572

Bachelor's thesis

College of Industrial Technology.-- Bachelor of engineering technology major in computer engineering technology: Technological University of the Philippines, 2025.

Includes bibliographic references and index.

The increasing loss of native tree species in urban environments, especially in
Metro Manila, underscores the urgent need for products that can provide new means to
access tools to raise awareness and identify and protect local flora. This study describes the
creation of a data-driven mobile application to identify five native trees from the

Philippines - Narra, Kamagong, Talisay, Banaba, and Ipil - using artificial intelligence-
based leaf image identification. Using the mobile application, users can accurately identify

trees and receive information on each tree species, including scientific names, ecological
preferences, and growth habits. The system was equipped with an intuitive interface that
includes a searchable tree database, as well as a map function to showcase important areas
in the Philippines with each tree species. Nevertheless, the identification model was
specifically trained only using native trees in and around the Arroceros Forest Park region,
thus limiting appropriate identification to only those species in that park. The AI
identification model includes a deep learning convolutional neural networks (CNN) trained
on a designated leaf image dataset that was curated, enhancing performance reliability. The
application was tested against a number of devices and Android versions, and the results
showed excellent functioning, responsive controls, and consistent behavior. Limitations
were the limitation to five tree species, and the identification accuracy was only relevant
to those in Arroceros. Testing was done against the software quality standards ISO/IEC
25010 and showed that the system’s accuracy, functionality, and usability were adequate.
This project is the start of an initiative for urban biodiversity by providing an object-based
and usable tool for native tree identification, as well as advocacy for tree conservation.

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