Year: 2024
Type: article
Abstract: Ostrich behavior is a key sign of their development and health.Quickly and accurately identifying it is crucial for growth monitoring and disease prevention.Computer vision technology, being real-time and non-contact, is widely used in livestock behavior recognition.But current methods, mainly for common livestock in simple settings, have drawbacks.This paper presents a method for ostrich behavior recognition using YOLOv7-MG.It aims to boost recognition efficiency and precision.Images of ostrich behavior are gathered from actual farms to create a dataset.The MobileOne network replaces the backbone of YOLOv7 to cut down computation and model size.Also, a GAM module is added to improve feature extraction in complex situations.The proposed method does better than YOLOv7 and other cattle behavior recognition systems.It has a relatively small model memory footprint and can precisely identify ostrich behavior.This lays the groundwork for ostrich disease prevention and management.
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: bronze
OpenAlex ID: W4405869039