Abstract

Trees provide numerous benefits to humans and other living organisms, making accurate tree species identification essential for their management and conservation. Identifying and describing tree species is also critical for genetic studies, biodiversity conservation, management practices, and regeneration strategies. Traditional methods of tree identification are often time-consuming and require significant expertise, highlighting the need for more efficient approaches. This research focuses on developing a QR code-based system for tree identification. Tree data were collected from Soil Conservation training School Miran Sahib. The system was designed using a three-tier architectural model. The database system, built with MS Word, formed the base tier. The middle tier, comprising the google drive, was employed as the scripting language to interact with the database. The client tier was developed Word file converted to PDF format. The QR code generator, ME QR an open-source software, utilized a QR code library to create QR code images. Each QR code was linked to a google drive database containing detailed information about tree species. These QR codes were attached to trees, allowing users to scan them and automatically access the corresponding tree information on the website.  A user survey conducted at the study site showed that the QR codes were user-friendly and made tree identification more engaging. The system increased public knowledge about trees and demonstrated potential for improving tree management practices.