e-space
Manchester Metropolitan University's Research Repository

    Application of deep learning for livestock behaviour recognition: a systematic literature review

    Rohan, Ali ORCID logoORCID: https://orcid.org/0000-0002-6496-0452, Rafaq, Muhammad Saad, Hasan, Md Junayed, Asghar, Furqan, Bashir, Ali Kashif ORCID logoORCID: https://orcid.org/0000-0001-7595-2522 and Dottorini, Tania (2024) Application of deep learning for livestock behaviour recognition: a systematic literature review. Computers and Electronics in Agriculture, 224. 109115. ISSN 0168-1699

    [img]
    Preview
    Published Version
    Available under License Creative Commons Attribution.

    Download (4MB) | Preview

    Abstract

    Livestock health and welfare monitoring is a tedious and labour-intensive task previously performed manually by humans. However, with recent technological advancements, the livestock industry has adopted the latest AI and computer vision-based techniques empowered by deep learning (DL) models that, at the core, act as decision-making tools. These models have previously been used to address several issues, including individual animal identification, tracking animal movement, body part recognition, and species classification. However, over the past decade, there has been a growing interest in using these models to examine the relationship between livestock behaviour and associated health problems. Several DL-based methodologies have been developed for livestock behaviour recognition, necessitating surveying and synthesising state-of-the-art. Previously, review studies were conducted in a very generic manner and did not focus on a specific problem, such as behaviour recognition. To the best of our knowledge, there is currently no review study that focuses on the use of DL specifically for livestock behaviour recognition. As a result, this systematic literature review (SLR) is being carried out. The review was performed by initially searching several popular electronic databases, resulting in 1101 publications. Further assessed through the defined selection criteria, 126 publications were shortlisted. These publications were filtered using quality criteria that resulted in the selection of 44 high-quality primary studies, which were analysed to extract the data to answer the defined research questions. According to the results, DL solved 13 behaviour recognition problems involving 44 different behaviour classes. 23 DL models and 24 networks were employed, with CNN, Faster R-CNN, YOLOv5, and YOLOv4 being the most common models, and VGG16, CSPDarknet53, GoogLeNet, ResNet101, and ResNet50 being the most popular networks. Ten different matrices were utilised for performance evaluation, with precision and accuracy being the most commonly used. Occlusion and adhesion, data imbalance, and the complex livestock environment were the most prominent challenges reported by the primary studies. Finally, potential solutions and research directions were discussed in this SLR study to aid in developing autonomous livestock behaviour recognition systems.

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    54Downloads
    6 month trend
    30Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record