数字农科院2.0

Early lameness detection in dairy cattle based on wearable gait analysis using semi-supervised LSTM-Autoencoder

文献类型: 外文期刊

作者: Kai Zhang;Shuqing Han;Jianzhai Wu;Guodong Cheng;Yali Wang;Saisai Wu;Jifang Liu

作者机构:

关键词: Autoencoder;Early lameness detection in dairy cows;Gait reconstruction;Long short-term memory;Time series anomaly detection

期刊名称: Computers and Electronics in Agriculture

ISSN: 0168-1699

年卷期: 2023 年 213 卷

页码:

收录情况: SCIE(2023版) ; ; EI(2023版)

摘要: Early lameness detection is crucial to ensure the welfare and productivity of dairy cows. However, current research on early lameness identification using wearable analysis relies on the limited robustness of indirect behavioral measures, which are susceptible to individual variations and imbalances in lameness samples. In this study, we propose a semi-supervised Long short-term memory (LSTM)-Autoencoder algorithm for early lameness detection in dairy cows through time series data reconstruction. We collected gait data from all four limbs of 30 dairy cows using four IMUs. A LSTM-Autoencoder with three LSTM hidden layers was trained to learn the time series features of healthy gaits. Each gait was reconstructed, and anomaly gaits exceeding a threshold were identified by comparing reconstructed gaits with actual gaits. The gait symmetry was measured by comparing the percentage of anomaly gait between opposite limbs as an indicator of lameness severity. With a high accuracy of 97.78% and a true negative rate of 98.33%, our integrated approach outperforms traditional methods in early lameness detection and lame limb identification, enabling real-time monitoring and timely identification of lameness. The study is the first attempt at using a time series anomaly detection framework with deep learning-based gait reconstruction for lameness detection. Wearable gait analysis offers portability and real-time capabilities, providing continuous, accurate, and comprehensive gait information unaffected by lighting and field-of-view limitations. This approach holds promise for enhancing animal welfare and optimizing management practices in the dairy industry through timely identification and continuous monitoring of lameness.

分类号:

  • 相关文献

[1]Auction-based deep learning-driven smart agricultural supply chain mechanism. Yu Feng,Dong Mei,Hua Zhao. 2023

[2]Prediction of Spatial Winter Wheat Yield by Combining Multiscale Time Series of Vegetation and Meteorological Indices. Xu, Hao,Yin, Hongfei,Liu, Jia,Wang, Lei,Feng, Wenjie,Song, Hualu,Fan, Yangyang,Qi, Kangkang,Liang, Zhichao,Li, Wenjie,Zhang, Xiaohu,Zhang, Rongjuan,Wang, Shuai. 2025

[3]DeepAT: A Deep Learning Wheat Phenotype Prediction Model Based on Genotype Data. Jiale Li,Zikang He,Guomin Zhou,Shen Yan,Jianhua Zhang. 2024

[4]A new comprehensive index for the assessment of mutton quality deterioration during storage using hyperspectral imaging combined with autoencoder-assisted self-supervised learning and generative network. Yi, Weiguo,Zhao, Xingyan,Yun, Xueyan,Wang, Bing,Li, Shaobo,Dong, Tungalag. 2025

[5]Cotton Yield Prediction with Gaussian Distribution Sampling and Variational AutoEncoder. Yaqi Lan,Xiudong Wang,Lei Gao,Xiaoliang Chen. 2025

作者其他论文 更多>>