Department of Agricultural Statistics, Faculty of Agricultural Sciences, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar - 751 030, Odisha, India
1Department of Biotechnology, Noida International University, Greater Noida, Gautam Budh Nagar - 201 310, Uttar Pradesh, India, Orcid Id : 0000-0002-2596-2578
2Centre for Multidisciplinary Research, Anurag University, Hyderabad - 500088, Telangana, India, Orcid Id : 0009-0007-1580-465X
3Quantum University Research Center, Quantum University, Roorkee - 247 667, Uttarakhand, India, Orcid Id : 0009-0006-2621-8763
4Centre of Research Impact and Outcome, Chitkara University, Rajpura - 140 417, Punjab, India, Orcid Id : https://orcid.org/0009-0001-0234-3205
The rapid advancement of artificial intelligence (AI) and 3D modeling technologies presents novel opportunities for enhancing the identification and classification of insect species. This study explores the integration of 3D modeling techniques and AI systems for the automated identification of insect species. By leveraging advanced imaging technologies and machine learning algorithms, this approach enhances biodiversity assessment, pest management, and ecological research. The implementation of 3D modeling in conjunction with AI systems has demonstrated significant improvements in species identification accuracy and efficiency. Case studies show that AI algorithms, trained on extensive datasets of 3D models, can achieve identification rates exceeding 90%, significantly reducing the reliance on expert entomologists. The ability to visualize insects in three dimensions enhances the understanding of their morphological features, providing insights into their ecological roles.
3D modeling, Artificial intelligence, Automated systems, Biodiversity, Conservation, Entomology, Insect identification, Morphological features, Pest management, Photogrammetry
