Digital DNA Tags: QR Code Generation For Insect Pest Species In Seri-Ecosystem

Abstract

Accurate and rapid identification of insect species is vital for biodiversity assessment, ecological research, and effective pest management. Traditional identification methods, which often depend on morphological examination can be time-consuming, labor-intensive and susceptible to human error. This study introduces an innovative approach that leverages the mitochondrial Cytochrome-c-oxidase I (COI) gene - a widely accepted molecular marker - for insect species identification, integrated with a QR code generation system using Python. The COI gene is highly conserved within species yet variable across taxa, making it a reliable genetic marker for distinguishing insect species. In this approach, COI gene sequences are extracted from reference databases and encoded into QR codes, which contain key taxonomic and ecological information such as species name, classification hierarchy, and habitat data. Upon scanning, the QR code provides immediate access to this information, facilitating quick identification and data retrieval in both laboratory and field settings. The system is developed using Python, incorporating bioinformatics tools for sequence analysis and a QR code generation library to convert genetic and taxonomic data into scannable codes. This integrated platform offers a fast, automated, and user-friendly alternative to conventional identification methods. Designed for entomologists, ecologists and field researchers, it enhances the efficiency of species monitoring and supports real-time biodiversity documentation. As a whole, the study demonstrates the potential of merging molecular techniques with digital technologies to streamline species identification and support conservation and ecological research efforts.