Sustainable AI &amp; Agricultural Technologies

Sarkadi, Ṣtefan; Moraru, Ionuţ; Manning, Louise

doi:10.1109/acsos-c58168.2023.00045

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…By applying data mining techniques to network security, stakeholders can develop more robust defense mechanisms against cyber-physical attacks, thereby safeguarding the infrastructure critical to sustainable systems. Sarkadi, Moraru, and Manning (2023) propose the use of evolutionary agent-based modeling to predict the effects of adopting AI-based agricultural technologies. This approach offers a holistic perspective on complex ecosystems and their resilience to socio-economic pressures.…”

Section: Future Directions In Green Technology and Cybersecuritymentioning

confidence: 99%

Innovation green technology in the age of cybersecurity: Balancing sustainability goals with security concerns

Excel G Chukwurah,

Chukwuekem David Okeke,

Cynthia Chizoba Ekechi

2024

Comput. sci. IT res. j.

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This study explores the critical intersection of cybersecurity measures and green technologies, aiming to assess their combined impact on sustainability goals and stakeholder implications. Employing a systematic literature review methodology, the research scrutinizes peer-reviewed journals, conference proceedings, and reports from reputable databases, focusing on publications from the year 2010 to 2024. The review identifies key themes, including the integration challenges and opportunities of cybersecurity within sustainable technologies, the evolving landscape of cybersecurity protocols, and the strategic implications for industry leaders, policymakers, and technologists. Key insights reveal the dual imperative of pursuing sustainability alongside security, highlighting the necessity of integrating robust cybersecurity measures without compromising the environmental benefits of green technologies. The study identifies significant challenges at this nexus, such as the rapid evolution of cyber threats and the complexity of embedding cybersecurity in green innovations. It also outlines opportunities for innovation and the development of a security-aware culture that supports environmental sustainability. Strategic recommendations are provided for stakeholders to navigate these complexities, emphasizing the importance of multidisciplinary approaches, continuous learning, and the development of policies that encourage the adoption of secure and sustainable technologies. The study concludes that fostering innovation in green technology requires a concerted effort to integrate cybersecurity measures effectively, underscoring the need for future research to expand the knowledge frontiers in this critical area. This research contributes to the ongoing dialogue on achieving environmental sustainability and technological resilience, offering a foundation for further exploration and action towards these dual objectives. Keywords: Cybersecurity, Green Technologies, Sustainable Technological, Stakeholder Security Concerns.

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Section: Future Directions In Green Technology and Cybersecuritymentioning

confidence: 99%

Innovation green technology in the age of cybersecurity: Balancing sustainability goals with security concerns

Excel G Chukwurah,

Chukwuekem David Okeke,

Cynthia Chizoba Ekechi

2024

Comput. sci. IT res. j.

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“…By leveraging AI technologies, farmers can make informed decisions about crop selection, disease management, and resource allocation, leading to reduced waste and increased productivity. Sarkadi, Moraru, and Manning (2023) explore the potential of sustainable AI and agricultural technologies in addressing global challenges such as climate change, food security, and biodiversity conservation. They propose the application of evolutionary agent-based modeling to understand the complex dynamics of the agricultural sector and the impact of AI-based technologies.…”

Section: Overview Of Ai Applications In Agricultural Supply Chainsmentioning

confidence: 99%

Ai-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing the Benefits and Challenges of Ai in Forecasting Demand and Optimizing Supply in Agriculture

Oluwafunmi Adijat Elufioye,

Chinedu Ugochukwu Ike,

Olubusola Odeyemi

et al. 2024

Comput. sci. IT res. j.

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This study provides a comprehensive review of the integration and impact of Artificial Intelligence (AI) in agricultural supply chains, focusing on its role in enhancing demand forecasting and optimizing supply. The primary objective was to assess how AI-driven predictive analytics transforms agricultural practices, addressing challenges, and shaping future trends. A systematic literature review and content analysis methodology were employed, utilizing academic databases and digital libraries to source peer-reviewed articles and conference papers published between 2014 and 2024. The inclusion criteria focused on studies related to AI applications in agricultural supply chains, while exclusion criteria filtered out non-peer-reviewed and irrelevant literature. Key findings reveal that AI significantly improves the accuracy and efficiency of demand forecasting and supply chain operations in agriculture. AI technologies, including machine learning and big data analytics, have led to advancements in real-time data analysis, predictive maintenance, and resource optimization. However, challenges such as data quality, infrastructure development, and skill gaps among agricultural professionals persist. The future landscape of AI in agriculture is marked by growth opportunities and challenges, including the need for equitable AI technology access and ethical considerations. The study recommends that industry leaders and policymakers invest in infrastructure, promote AI research and development, and provide training to facilitate AI adoption. Future research should focus on developing robust AI models tailored to agriculture, exploring AI's integration with emerging technologies, and assessing AI's long-term socio-economic impacts. This study contributes to understanding AI's current applications and future potential in transforming agricultural supply chains, offering valuable insights for stakeholders in the agricultural sector. Keywords: Artificial Intelligence, Agricultural Supply Chains, Predictive Analytics, Demand Forecasting.

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A Socio-Technical Perspective on Fostering Sustainable Community Development via Social Capital and Social Cohesion

Lloyd,

Lewis

2024

2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)

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Sustainable AI & Agricultural Technologies

Cited by 3 publications

References 15 publications

Innovation green technology in the age of cybersecurity: Balancing sustainability goals with security concerns

Innovation green technology in the age of cybersecurity: Balancing sustainability goals with security concerns

Ai-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing the Benefits and Challenges of Ai in Forecasting Demand and Optimizing Supply in Agriculture

A Socio-Technical Perspective on Fostering Sustainable Community Development via Social Capital and Social Cohesion

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