Author(s)

Fentian Li ¹

Affiliation(s)

¹ The Tourism College of Changchun University, Changchun, Jilin, 130607, China

Corresponding Author

Fentian Li

ABSTRACT

With the increasing awareness of ecological protection and the increasing demand for agricultural intelligence, animal and plant identification technology has become a research hotspot. Traditional identification methods rely on manual experience, are inefficient and lack accuracy. This article explores an intelligent identification glasses system for animals and plants based on machine vision. By integrating cameras, AR display modules and deep learning models, it achieves real-time and efficient identification of animals and plants in the natural environment. The system uses the improved YOLOv5 algorithm, combined with the attention mechanism and multi-scale detection technology, to significantly improve the recognition accuracy and speed. Experiments show that the system's recognition accuracy and processing speed of animals and plants in complex environments are significantly improved, providing a convenient identification tool for non-professionals. This research not only provides innovative solutions for biodiversity conservation, agricultural production and science education, but also provides a practical reference for the application of target detection technology on edge computing devices.

KEYWORDS

Machine Vision; YOLOv5; Intelligent Recognition Glasses; Deep Learning; Multi-Scale Detection Technology

CITE THIS PAPER

Fentian Li, Research on Intelligent Identification Glasses System for Animals and Plants Based on Machine Vision. Automation and Machine Learning (2025) Vol. 6: 32-37. DOI: http://dx.doi.org/10.23977/autml.2025.060204.

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