ARTIFICIAL INTELLIGENCE IN THE WILD: MONITORING AND NATURE CONSERVATION

ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ В ДИКОЙ ПРИРОДЕ: МОНИТОРИНГ И ОХРАНА ПРИРОДЫ

Угвуаньи Десмонд Чинаэмере

студент Средняя школа для мальчиков Осисатех, штат Энугу,

Федеративная Республика Нигерия, г. Аморджи найк Эмене

Эджиофор Джонсон Чидибере

студент, Факультет сестринского дела, Российский университет дружбы народов имени Патриса Лумумбы,

РФ, г. Москва

ABSTRACT

The article provides an overview of the modern approach and tools for protecting and preserving wildlife using AI technologies, the challenges faced by specialists in attempts to develop AI tools, as well as current global examples of wildlife conservation projects based on AI. The article consists of an introduction, main part, conclusion and a list of sources and literature.

АННОТАЦИЯ

В статье представлен обзор на современный подход и инструменты защиты и сохранения дикой природы при помощи ИИ технологий, вызовы, с которыми сталкиваются специалисты в попытках разработки ИИ-инструментов, а также актуальные мировые примеры проектов по сохранению дикой природы, работающих на основе ИИ. Статья состоит из введения, основной части, заключения и списка источников и литературы.

Keywords: wildlife, AI technologies, endangered species, data analysis, critical analysis, interdisciplinary approach.

Ключевые слова: дикая природа, ИИ-технологии, вымирающие виды, анализ данных, критический анализ, междисциплинарный подход.

Introduction

Nowadays artificial intelligence tools involvement in different spheres of life has reached its pinnacle. Such sphere as protection of wild nature has become no exception, because at the moment wild nature of all continents needs protection and control of species population primarily due to human activity [5].

The main areas of work on the conservation of wildlife are: registration and monitoring of individuals, development of predictive methods, development of methods for monitoring and combating poaching, as well as an equally relevant topic today - awakening civil initiative, responsibility and awareness of the state of wildlife, instilling values oriented towards the protection and enhancement of the natural habitat.

The object of this study is AI as a tool for monitoring and conservation of wildlife. The subject of the study is the spheres, possibilities, and advantages of using AI algorithms to save, preserve and increase the wealth of wildlife.

The aim of the study is to discuss current methods of using AI for monitoring and conservation of wildlife, and to identify promising areas for the application of AI tools in conservation of wildlife diversity. To achieve the stated goal, the following research objectives have been formulated:

1. To study existing methods of applying AI tools to protect wildlife around the world;

2. To study the main problems and challenges faced by wildlife monitoring and conservation specialists using real examples of global practice;

3. To identify promising areas of application of AI algorithms in the field of wildlife protection.

Real-time tracking

Registration and tracking are traditionally a basic tool for preserving and enhancing wildlife. This method of monitoring has been known to mankind since the inception of forestry and gamekeeping. However, today, camera traps operating on the basis of AI have simplified the task many times over. The operating principle of camera traps can be divided into the following stages:

1. Automatic detection: when an animal passes close to the camera, it automatically detects the movement using light and sound-sensitive sensors and transmits the information to the server.
2. Image analytics: AI algorithms begin processing images and determine the type of animal on the camera, the latest generations of AI are able to determine a specific individual that is in front of the camera by the smallest features. In this case, the work is simplified for employees, and the process itself is accelerated.
3. Then the information is loaded into the big data system in real time, allowing research to be carried out on the most relevant data at the time [2].

An example of such an algorithm is the application Wildbook [9]. It allows the use of animal images to create a database based on the collected information and provides a complete analysis of the behavior and movement of species, which greatly simplifies the task of finding behavior patterns and predicting them.

Forecasts and conservation strategies: advanced predictive models

In addition to collecting information, AI is also capable of analyzing the collected data and providing an analysis report in different forms, as well as gaining insights into the state of wildlife. This approach allows for more informed decisions and strategies in emergency and planned response situations. AI at this level is also capable of making predictions about the dynamics of a given situation. AI helps predict future trends in wildlife populations. An example of such AI is EarthRanger, which collects data on known poaching incidents and predicts future incidents. When acoustic sensors are connected to a database, it can monitor and record poaching incidents in real time [3].

Thus, AI in the field of wildlife conservation can be divided into three basic categories: AI that records activity in the area and compiles data into a database. The second category of AI allows you to analyze the data, divide it into categories, compare it with previously obtained data. And the third category allows you to identify future trends, think through strategies and offer ready-made solutions. Table 1 describes in detail all the categories of AI used in wildlife conservation work [1].

Table 1.

Types of AI and conservation measures using them

Challenges and future directions of Wildlife conservation AI software

Despite the obvious advantages of using AI in monitoring and preserving wildlife, there are a number of challenges that the scientific community faces today. The first of these is the quality of the data obtained. Data quality is a basic necessity in AI analytics. From the quality of the data used in training AI. The data provided for analysis should be as truthful and unbiased as possible [7].

The second issue of AI in the wild is the complexity of the models themselves. With the complication of the technological device of AI, so-called "hallucinations" arise - results that have nothing in common with reality, it is also quite difficult to predict the result produced by AI and at this stage requires careful observations from a person and is not completely autonomous in its work [4].

However, it is worth separately highlighting the socially significant aspects of using AI for monitoring and preserving wildlife. The first of these is the use of AI to generate content on the protection and conservation of wildlife [6]. AI helps to define the target audience and form a news feed on social networks for ordinary people, thereby forming their point of view regarding the problem of endangered species, becomes an educational element in the process of growing up of young users and forms habits of conscious behavior in the wild and at home, which certainly affects wildlife. [8]

Conclusion

In conclusion, AI is successfully integrated into wildlife conservation work, which has resulted in significant progress in the fight to preserve species habitats around the world over the past 10 years. As in any other area where AI is used, there are challenges and difficulties that specialists around the world still have to work on. However, in addition to its direct function of directly monitoring and analyzing the state of nature, AI implemented in wildlife conservation projects has a social and political function, AI has become part of the process of forming public opinion and educating society on issues of wildlife conservation, and also contributes to legislative activity.

References:

1. Adema P., Exploring the potential of Artificial Intelligence for nature conservation, National Committee of Netherlands, 2023 https://www.iucn.nl/en/blog/exploring-the-potential-of-artificial-intelligence-for-nature-conservation/
2. Chisom O., Biu P., Umoh A., Reviewing the role of AI in environmental monitoring and conservation: A data-driven revolution for our planet, World Journal of AdvancedResearch and Reviews, 2024
3. EarthRanger, The Allen Institute for Artificial Intelligence, 2024 https://www.earthranger.com/
4. Fritz K., 5 Ways AI is Helping Wildlife Conservation, AI Time Journal, 2022
5. Gohil T., AI in Wildlife Research: Tracking and Conservation Strategies, ITmunch, 2024 https://itmunch.com/ai-wildlife-research-tracking-conservation-strategies
6. GPT4, AI and Wildlife Conservation GPT-AI-Driven Wildlife Conservation, 2024
7. Tuia D., Kellenberger B., Beery S., Perspectives in machine learning for wildlife conservation, Nature, 2024
8. Velasco-Montero D., Fernandez-Berni J., Reliable and efficient integration of AI into camera traps for smart wildlife monitoring based on continual learning, Elsevier, 2024 https://www.sciencedirect.com/science/article/pii/S1574954124003571
9. Wildbook artificial intelligence software, Smart Earth Project, 2024 https://www.wildme.org/wildbook.html