Work

Implementation of artificial intellect for bird pest species detection and monitoring

Year: 2024

Type: article

Abstract: This study aimed to develop a real-time method for detecting and selecting birds in video images using artificial intelligence. The objectives included creating a reliable method for isolating bird signals against varying terrain backgrounds using neural networks, estimating bird numbers in frames through AI-driven threshold techniques, and proposing a solution for managing pest bird populations by analyzing video data to control electronic deterrents. Throughout the research, we identified the bird species present on the premises of brewery across different seasons, compiled an annotated species list, and established a database of granary birds. Leveraging the YOLO architecture based on artificial intelligence, we developed a program for bird detection in low-resolution, low-quality images. The system underwent laboratory and field testing to validate its effectiveness.

Source: Acta Biologica Sibirica (Altai State University)

DOI: 10.5281/zenodo.13831320

Authors: Shapetko, Elena V., Belozerskikh, Vasiliy V., Siokhin, Valery D.

Institutions: Altai State University, Bogdan Khmelnitsky Melitopol State Pedagogical University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Zero hunger, Life on land

Open Access status: green

Funder: Ministry of Science and Higher Education of the Russian Federation