Integrating Policy, Governance, and Law in Regulating AI for Sustainable Environmental Protection DOI
Shashwata Sahu, Navonita Mallick, Sanghamitra Patnaik

et al.

Practice, progress, and proficiency in sustainability, Journal Year: 2024, Volume and Issue: unknown, P. 157 - 184

Published: Nov. 1, 2024

Artificial Intelligence (AI) transforms environmental conservation by enhancing sustainability and efficiency in addressing critical challenges. However, AI must be regulated within a robust policy, governance, law framework to harness its full potential. This paper explores the complex interaction of these elements regulating for sustainable protection. Through doctrinal methodology, it examines legal texts, policies, governance structures assess their adequacy guiding applications ecological contexts. The findings reveal significant gaps current frameworks, underscoring need integrated, enforceable guidelines that ensure ethical deployment. research concludes advocating comprehensive regulatory tools align with goals.

Language: Английский

Reviewing the role of AI in environmental monitoring and conservation: A data-driven revolution for our planet DOI Creative Commons

Onyebuchi Nneamaka Chisom,

Preye Winston Biu,

Aniekan Akpan Umoh

et al.

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(1), P. 161 - 171

Published: Jan. 4, 2024

The rapid increase in human activities is causing significant damage to our planet's ecosystems, necessitating innovative solutions preserve biodiversity and counteract ecological threats. Artificial Intelligence (AI) has emerged as a transformative force, providing unparalleled capabilities for environmental monitoring conservation. This research paper explores the applications of AI ecosystem management, including wildlife tracking, habitat assessment, analysis, natural disaster prediction. AI's role conservation includes resource conservation, species identification. algorithms analyze camera trap footage, drone imagery, GPS data identify estimate population sizes, leading improved anti-poaching efforts enhanced protection diverse species. Habitat assessment involve AI-powered image which aids assessing forest health, detecting deforestation, identifying areas need restoration. Biodiversity analysis identification are achieved through that acoustic recordings, DNA (eDNA), footage. These innovations different species, assess levels, even discover new or endangered flood prediction systems provide early warnings, empowering communities with better preparedness evacuation efforts. Challenges, such quality availability, algorithmic bias, infrastructure limitations, acknowledged opportunities growth improvement. In policy regulation, advocates clear frameworks prioritizing privacy security, transparency, equitable access. Responsible development ethical use emphasized foundational pillars, ensuring integration into aligns principles fairness, societal benefit.

Language: Английский

Citations

40

Worlds that collide: conservation applications of behaviour and culture in human–wildlife interactions DOI
Estelle Meaux, Culum Brown, Sarah L. Mesnick

et al.

Philosophical Transactions of the Royal Society B Biological Sciences, Journal Year: 2025, Volume and Issue: 380(1925)

Published: May 1, 2025

The behaviour of both humans and wildlife is central to the conservation biodiversity because requires human actions at multiple scales. In species with evidence socially learned culture, juxtaposition animal culture increases complexity human-wildlife interactions their investigation but also offers opportunities mitigate negative interactions. this paper, we consider language used analyse human-animal review effect behaviours on those We investigate how knowledge theory from behavioural studies can be negotiate complex between wildlife, providing specific examples mined for developing policies regarding highlight that are such a key target conservation. Integrating social learning into research scope leverage gaps, misconceptions concerns targeted, relevant meaningful.This article part theme issue 'Animal culture: in changing world'.

Language: Английский

Citations

4

Smart farming and Artificial Intelligence (AI): how can we ensure that animal welfare is a priority? DOI Creative Commons
Marian Stamp Dawkins

Applied Animal Behaviour Science, Journal Year: 2025, Volume and Issue: unknown, P. 106519 - 106519

Published: Jan. 1, 2025

Language: Английский

Citations

3

Elephant Sound Classification Using Deep Learning Optimization DOI Creative Commons
Hiruni Dewmini, Dulani Meedeniya, Charith Perera

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(2), P. 352 - 352

Published: Jan. 9, 2025

Elephant sound identification is crucial in wildlife conservation and ecological research. The of elephant vocalizations provides insights into the behavior, social dynamics, emotional expressions, leading to conservation. This study addresses classification utilizing raw audio processing. Our focus lies on exploring lightweight models suitable for deployment resource-costrained edge devices, including MobileNet, YAMNET, RawNet, alongside introducing a novel model termed ElephantCallerNet. Notably, our investigation reveals that proposed ElephantCallerNet achieves an impressive accuracy 89% classifying directly without converting it spectrograms. Leveraging Bayesian optimization techniques, we fine-tuned parameters such as learning rate, dropout, kernel size, thereby enhancing model's performance. Moreover, scrutinized efficacy spectrogram-based training, prevalent approach animal classification. Through comparative analysis, processing outperforms methods. In contrast other literature primarily single caller type or binary identifies whether voice not, solution designed classify three distinct caller-types namely roar, rumble, trumpet.

Language: Английский

Citations

1

Computational methods for detecting insect vibrational signals in field vibroscape recordings DOI
Matija Marolt, Matevž Pesek,

Rok Šturm

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: 86, P. 103003 - 103003

Published: Jan. 18, 2025

Language: Английский

Citations

1

Active interactions between animals and technology: biohybrid approaches for animal behaviour research DOI Creative Commons
Marina Papadopoulou,

M. Ball,

Palina Bartashevich

et al.

Animal Behaviour, Journal Year: 2025, Volume and Issue: unknown, P. 123160 - 123160

Published: April 1, 2025

Language: Английский

Citations

1

Revolutionizing animal sciences: Multifaceted solutions and transformative impact of AI technologies DOI
Ebrahim Talebi,

Maryam Khosravi Nezhad

CABI Reviews, Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 5, 2024

Abstract In recent years, the integration of artificial intelligence (AI) has markedly bolstered productivity, especially in agriculture, mitigating environmental impacts like greenhouse gas emissions. This shift employs a range tech, IT, sensors, robotics, and AI, boosting output while curbing negative effects. Challenges persist, notably food scarcity climate threats for growing global population. By 2050, two billion more people will need sustenance, necessitating urgent agricultural innovation. article reviewed databases from 1985 to 2023 (Google Scholar, Scopus, ISI Web Knowledge), analyzing AI’s role agriculture. Keywords precision feeding, welfare, animal husbandry, management were used systematic literature review. Findings highlight pivotal addressing shortages. Investment emerging is crucial sustainable supply.

Language: Английский

Citations

3

An Overview of AI Applications in Wildlife Conservation DOI
Binod Kumar, Oindrilla Ghosh

Advances in environmental engineering and green technologies book series, Journal Year: 2025, Volume and Issue: unknown, P. 19 - 48

Published: Jan. 10, 2025

The integration of artificial intelligence (AI) into wildlife conservation has revolutionized methodologies for monitoring species, enhancing habitat management, and combating poaching. This chapter examines various AI applications that contribute to the protection preservation biodiversity. Remote sensing technologies, powered by machine learning algorithms, assist in assessing health tracking changes over time. AI-driven image recognition tools enable identification individual animals from camera trap photos, facilitating more accurate population estimates behavioral studies. Moreover, predictive analytics play a crucial role forecasting human-wildlife conflicts informing proactive management strategies. synthesis technologies demonstrates their potential enhance efforts, optimize resource allocation, ultimately foster effective initiatives. ongoing advancement this field promises create innovative solutions some most pressing challenges.

Language: Английский

Citations

0

Application of geographic information system and remote sensing technology in ecosystem services and biodiversity conservation DOI
Maqsood Ahmed Khaskheli, Mir Muhammad Nizamani,

Umed Ali Laghari

et al.

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 97 - 122

Published: Jan. 1, 2025

Language: Английский

Citations

0

Modeling Habitat Suitability for Endangered Herb (Salvia leriifolia Benth) Using Innovative Hybrid Machine Learning Algorithms DOI Creative Commons

Emran Dastres,

Hamidreza Rabiei‐Dastjerdi, Hassan Esmaeili

et al.

Environmental and Sustainability Indicators, Journal Year: 2025, Volume and Issue: unknown, P. 100694 - 100694

Published: April 1, 2025

Language: Английский

Citations

0