Development of A Qr Code System for Tree Species Identification
Bibliographic record
Abstract
Trees provide a wide range of benefits to humans and other living organisms. An accurate method tree species identification will improve their management and conservation. Also, tree identification and description are crucial for genetic study, biodiversity conservation, management and regeneration strategies. The conventional methods of tree identification are time-consuming and requires a high level of expertise, necessitating development of a more efficient tree identification means. In this research, a QR code system for tree identification was developed. Tree data were collected from campuses of two tertiary institutions in Akure, Nigeria: Federal University of Technology and Federal College of Agriculture. System design was built around a three-tier architectural model. PostgresSQL was used as the Database System, the lowest tier. The Middle tier is the Web Server, Apache HTTP Server. Php 8.1 was the scripting language that communicates with the database. For the Client tier, HTML, CSS and Javascript were used. The QR code generator was developed using PHP 8.1. The PHP script used a QR code library to generate the QR code image. The QR code is linked to the website database containing all tree species information. The generated QR codes were attached to trees, and when scanned, the website is automatically launched and the tree information is retrieved. A survey was conducted to get end-users’ feedback within the study sites. The results obtained revealed that the QR codes are easy to use, and can make tree identification more interesting, thus increasing people’s knowledge about trees and improving Trees management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".