Artificial intelligence tools to assess different levels of activity performed by semi-wild horses in grassland ecosystems.
Abstract: In order to understand the role of horses in ecosystems and to effectively use their grazing in the protection of grasslands, it is important to assess where they primarily stay, followed by whether these habitats are used for grazing or resting. The main goal of the study was the model development based on artificial intelligence tools which allow to distinguish the basic levels of activity performed by horses using data from an accelerometer mounted in a collar worn by animals. The model calibration was based on direct observations of five randomly selected Polish primitive horse mares. In order to create a model that allows for classification into three groups of behaviours: grazing, resting, and moving, an approach based on machine learning, one of the basic technologies of artificial intelligence, was used. The carried out analyses allowed for the determination of the most important features, among the fourteen determined from raw X, Y, and Z axis acceleration values across 5-s measurements. The recommended method for the classification of behaviours of primitive Konik horses based on the selection of variables observed from the accelerometer is the CART method, whereas the most accurate tool for its construction is learning neural networks. Our research indicates the usefulness of the accelerometer and proposed artificial intelligence methods in distinguishing the main activities performed by horses.
© 2025. The Author(s).
Publication Date: 2025-07-16 PubMed ID: 40665113PubMed Central: PMC12263774DOI: 10.1007/s10661-025-14363-1Google Scholar: Lookup
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Summary
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This research developed an artificial intelligence (AI) model to assess the levels of activity performed by semi-wild horses, enabling a better understanding of their role and behavior in grassland ecosystems, which facilitates more effective grazing management strategies.
Research Objective and Methodology
- The main objective of the study was to develop an AI-based model that accurately categorizes the horses’ behaviors into primary activities: grazing, resting, and moving. To achieve this, the researchers collected data using accelerometers attached to the collars worn by the semi-wild Polish primitive horse mares.
- The model development process involved calibrating the accelerometer data against direct observations of the horses’ activities. This approach allowed for machine learning techniques to accurately classify activities based on the accelerometer’s raw X, Y, and Z axis acceleration values from 5-second measurements.
Key Findings and Recommendations
- The analysis identified the most crucial features out of fourteen derived from raw acceleration values that helped distinguishing the horses’ main activities. These features were used to teach the AI model to classify the horses’ behaviors accurately.
- The study recommends using the Classification And Regression Tree (CART) method for classifying the behaviors of primitive Konik horses based on accelerometer data. Notably, learning neural networks was found to be the most effective tool for building the model.
- The research underscores the potential of accelerometers and AI methods in identifying the main activities of semi-wild horses, suggesting their valuable application in monitoring and managing these animals in grassland ecosystems.
Implications of the Research
- The findings of this study could significantly improve the understanding of semi-wild horses’ behavior in their natural habitats. The application of AI tools could make it easier for ecologists and other stakeholders to monitor and manage these horses, thus promoting the preservation of grassland ecosystems.
- The developed AI model also demonstrates the potential for similar applications in other wildlife monitoring efforts, thereby expanding the utility of these technologies in conservation and ecosystem management.
Cite This Article
APA
Chodkiewicz A, Prończuk M, Studnicki M.
(2025).
Artificial intelligence tools to assess different levels of activity performed by semi-wild horses in grassland ecosystems.
Environ Monit Assess, 197(8), 922.
https://doi.org/10.1007/s10661-025-14363-1 Publication
Researcher Affiliations
- Department of Agronomy, Institute of Agriculture, Warsaw University of Life Sciences, 159 Nowoursynowska Str., 02-776, Warsaw, Poland. anna_chodkiewicz@sggw.edu.pl.
- , Warsaw, Poland.
- Department of Biometry, Institute of Agriculture, Warsaw University of Life Sciences, 159 Nowoursynowska Str., 02-776, Warsaw, Poland.
MeSH Terms
- Animals
- Horses / physiology
- Artificial Intelligence
- Grassland
- Environmental Monitoring / methods
- Behavior, Animal
- Ecosystem
Conflict of Interest Statement
Declarations. Ethics approval: All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. A statement on the welfare of animals if the research involved animals: No approval of research ethics committees was required to accomplish the goals of this study because experimental work did not involve capturing and chemical immobilisation of the horses. Competing interests: The authors declare no competing interests. Clinical trial number: Not applicable.
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