Work

Cow Behavior Recognition Method Based on Multi-Source Perceptual Information Fusion

Year: 2026

Type: article

Abstract: This study proposes a multi-source perceptual information fusion method to improve the accuracy and stability of dairy cow behavior monitoring. Existing machine vision approaches are often affected by lighting conditions, occlusion, and complex cowshed environments, while single wearable inertial measurement unit (IMU) devices may confuse similar behaviors such as eating, ruminating, standing, and lying. To address these limitations, a wireless collar was developed to synchronously collect nine-axis IMU data and ultra-wideband (UWB) ranging data in real time. Combined with manual behavioral observations, a dataset covering seven behaviors-eating, ruminating, standing, lying, drinking, sleeping, and lateral trunk contact-was constructed. By integrating neck-motion features extracted from the IMU data with spatial-distance features obtained from the UWB data, an IMU-UWB dual-branch fusion model was developed to automatically classify dairy cow behaviors. The results indicate that the proposed method can effectively reduce confusion among similar behaviors and improve the recognition of behaviors with limited samples. This approach enables more comprehensive assessment of dairy cows' daily activities and health status, providing technical support for health monitoring, early disease warning, and intelligent dairy farm management.

Source: Veterinary Sciences

DOI: 10.3390/vetsci13090856

Authors: Xiuyan Zhao, Hongzheng Sun, Kaixing Zhang, Junchi Sun, Yilong Lin, Jianzhu Liu

Institutions: Shandong Agricultural University, Jining University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $2,534