Use of computational vision and UAVs in livestock: a Literature review

Weber, Fabricio de Lima; Cagnin, Maria Istela; Paiva, Débora Maria Barroso; Weber, Vanessa; Medeiros, S. R. de; Gomes, Rodrigo da Costa; Pistori, Hemerson

doi:10.23919/cisti.2019.8760955

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…These surveys on PLF systems mainly focused on a few aspects of animal behaviour monitoring (grazing or health) using sensor data. A review by [21] investigated existing knowledge about livestock identification and counting-based computer vision techniques using data captured by UAVs. Listed the potential applications of ML algorithms in precision livestock farming, particularly for the analysis of grazing and animal health, and the main form of data acquisition in PLF This review lacks the critical evaluation of DL algorithms for different PLF applications, their ability, and the potential of UAVs for data acquisition.…”

Section: A Research Objectivesmentioning

confidence: 99%

“…Listed the potential applications of ML algorithms in precision livestock farming, particularly for the analysis of grazing and animal health, and the main form of data acquisition in PLF This review lacks the critical evaluation of DL algorithms for different PLF applications, their ability, and the potential of UAVs for data acquisition. [21] It presents the computational vision technologies and tools used for livestock identification and counting using data captured by UAVs This review lacks the critical evaluation, existing research gaps, and future search opportunities [22] This…”

Section: A Research Objectivesmentioning

confidence: 99%

“…The findings showed numerous potential uses of UAVs and DL technologies to support livestock PLF activities to detect, localize, and classify. The future of livestock farming can hold swarms of UAVs [143] and hybrid aerial-ground actors that could support data collection and monitoring tasks [21], [144]. These will mitigate increased processing delays due to computational processingintensive tasks like visual identification and tracking of animals.…”

Section: Prospective Limitations and Future Solutionsmentioning

confidence: 99%

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A Systematic Literature Review on the Use of Deep Learning in Precision Livestock Detection and Localization Using Unmanned Aerial Vehicles

et al. 2022

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With the ever-increasing importance of dairy and meat production, precision livestock farming (PLF) using advanced information technologies is emerging to improve farming production systems. The latest automation, connectivity, and artificial intelligence developments open new horizons to monitor livestock in the pasture, controlled environments, and open environments. Due to the significance of livestock detection and tracking, this systematic review extracts and summarizes the existing deep learning (DL) techniques in PLF using unmanned aerial vehicles (UAV). In the context of livestock recognition studies, UAVs are receiving growing attention due to their flexible data acquisition and operation in different conditions. This review examines the implemented DL architectures and scrutinizes the broadly exploited evaluation metrics, attributes, and databases. The classification of most UAV livestock monitoring systems using DL techniques is in three categories: detection, classification, and localization. Correspondingly, this paper discusses the future benefits and drawbacks of these DL-based PLF approaches using UAV imagery. Additionally, this paper describes alternative methods used to mitigate issues in PLF. The aim of this work is to provide insights into the most relevant studies on the development of UAV-based PLF systems focused on deep neural network-based techniques.

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Section: A Research Objectivesmentioning

confidence: 99%

Section: A Research Objectivesmentioning

confidence: 99%

Section: Prospective Limitations and Future Solutionsmentioning

confidence: 99%

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A Systematic Literature Review on the Use of Deep Learning in Precision Livestock Detection and Localization Using Unmanned Aerial Vehicles

et al. 2022

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“…However, to speed up the process, more tools are required. Autonomous learning is another additional technique to the previous two that increases its technological advance [174]. The precedent processes of the land transportation of cattle, such as the conditions in the farms, nutrition, surveillance, monitoring, and mixing with unknown animals, among other factors, are essential concerning animal welfare and are interrelated to the adjacent problems which occur throughout the supply chain [175].…”

Section: Artificial Vision and Uav (Unmanned Air Vehicles)mentioning

confidence: 99%

Disciplinary Categorization of the Cattle Supply Chain—A Review and Bibliometric Analysis

et al. 2022

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Global warming is a problem that threatens humanity, with livestock being one of the causes. A systematic literature review was carried out by using some appropriate elements of the PRISMA statement to identify disciplines that work to mitigate the effects of the livestock industry by organizing them according to their approach to addressing this problem. The main objective is to find information and classify the disciplines, papers, literature review methodologies, research gaps, authors, and journals developing the management of the cattle supply chain. This paper could analyze and mitigate the adverse effects on society and the environment generated by the industry, organizing them according to their approach. Twenty databases were consulted between March and May 2020, from which 146 review documents were chosen. The papers reviewed were published between 2003 and 2020. The eligibility criteria for selection were open access to the full text, publication in an indexed journal, and a focus on any discipline related to cattle. The unselected papers did not have DOIs or duplicates, and those focused on other types of meat and book chapters. Subsequently, the information in the selected papers was described and consolidated, and these papers had 602 authors and were from 99 journals. Next, a discipline categorization was proposed. The results were organized, showing that among all the analysis criteria, the category of veterinary medicine had the best results in terms of indicators; therefore, additional research is needed on the other disciplines, especially in culture, technology, management, quality control, tanneries, and transportation, as there was less research within these disciplines. It is recommended that research on a mix of the different proposed disciplines be conducted. The proposed categorization’s main contribution is to identify and group the cattle supply chain’s different disciplines and the definition of research gaps organized under a structure organizational management model. Finally, a multicriteria selection methodology must be used that prioritizes the discipline categories proposed in this review to guide future research.

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Automatic Classification of Body Condition Score of Cows: A Systematic Literature Review

Viana,

Araújo,

Melquíades

et al. 2023

2023 18th Iberian Conference on Information Systems and Technologies (CISTI)

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Use of computational vision and UAVs in livestock: a Literature review

Cited by 3 publications

References 13 publications

A Systematic Literature Review on the Use of Deep Learning in Precision Livestock Detection and Localization Using Unmanned Aerial Vehicles

A Systematic Literature Review on the Use of Deep Learning in Precision Livestock Detection and Localization Using Unmanned Aerial Vehicles

Disciplinary Categorization of the Cattle Supply Chain—A Review and Bibliometric Analysis

Automatic Classification of Body Condition Score of Cows: A Systematic Literature Review

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