Artificial Intelligence in Sustainable Vertical Farming
- URL: http://arxiv.org/abs/2312.00030v1
- Date: Fri, 17 Nov 2023 22:15:41 GMT
- Title: Artificial Intelligence in Sustainable Vertical Farming
- Authors: Hribhu Chowdhury, Debo Brata Paul Argha, Md Ashik Ahmed
- Abstract summary: The paper provides a comprehensive exploration of the role of AI in sustainable vertical farming.
The review synthesizes the current state of AI applications, encompassing machine learning, computer vision, the Internet of Things (IoT), and robotics.
The implications extend beyond efficiency gains, considering economic viability, reduced environmental impact, and increased food security.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As global challenges of population growth, climate change, and resource
scarcity intensify, the agricultural landscape is at a critical juncture.
Sustainable vertical farming emerges as a transformative solution to address
these challenges by maximizing crop yields in controlled environments. This
paradigm shift necessitates the integration of cutting-edge technologies, with
Artificial Intelligence (AI) at the forefront. The paper provides a
comprehensive exploration of the role of AI in sustainable vertical farming,
investigating its potential, challenges, and opportunities. The review
synthesizes the current state of AI applications, encompassing machine
learning, computer vision, the Internet of Things (IoT), and robotics, in
optimizing resource usage, automating tasks, and enhancing decision-making. It
identifies gaps in research, emphasizing the need for optimized AI models,
interdisciplinary collaboration, and the development of explainable AI in
agriculture. The implications extend beyond efficiency gains, considering
economic viability, reduced environmental impact, and increased food security.
The paper concludes by offering insights for stakeholders and suggesting
avenues for future research, aiming to guide the integration of AI technologies
in sustainable vertical farming for a resilient and sustainable future in
agriculture.
Related papers
- Harnessing Artificial Intelligence for Sustainable Agricultural
Development in Africa: Opportunities, Challenges, and Impact [0.0]
The study navigates through the dynamic landscape of AI applications in agriculture.
Opportunities such as precision farming, crop monitoring, and climate-resilient practices are examined.
Ethical considerations and policy implications are also discussed.
arXiv Detail & Related papers (2024-01-03T23:02:13Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - Towards Artificial General Intelligence (AGI) in the Internet of Things
(IoT): Opportunities and Challenges [55.82853124625841]
Artificial General Intelligence (AGI) possesses the capacity to comprehend, learn, and execute tasks with human cognitive abilities.
This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the Internet of Things.
The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education.
arXiv Detail & Related papers (2023-09-14T05:43:36Z) - The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence [67.70415658080121]
Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole.
AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
arXiv Detail & Related papers (2023-07-09T21:16:56Z) - Artificial Intelligence for Real Sustainability? -- What is Artificial
Intelligence and Can it Help with the Sustainability Transformation? [0.0]
This article briefly explains, classifies, and theorises AI technology.
It then politically contextualises that analysis in light of the sustainability discourse.
It argues that AI can play a small role in moving towards sustainable societies.
arXiv Detail & Related papers (2023-06-15T15:40:00Z) - Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities [86.89427012495457]
We review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry.
We present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery.
We highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI.
arXiv Detail & Related papers (2023-05-03T05:16:54Z) - AI Maintenance: A Robustness Perspective [91.28724422822003]
We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance.
We propose an AI model inspection framework to detect and mitigate robustness risks.
Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
arXiv Detail & Related papers (2023-01-08T15:02:38Z) - Towards Sustainable Artificial Intelligence: An Overview of
Environmental Protection Uses and Issues [0.0]
This paper describes the paradox of an energy-consuming technology serving the ecological challenges of tomorrow.
It draws on numerous examples from AI for Green players to present use cases and concrete examples.
The environmental dimension is part of the broader ethical problem of AI, and addressing it is crucial for ensuring the sustainability of AI in the long term.
arXiv Detail & Related papers (2022-12-22T14:31:48Z) - A Survey on AI Sustainability: Emerging Trends on Learning Algorithms
and Research Challenges [35.317637957059944]
We review major trends in machine learning approaches that can address the sustainability problem of AI.
We will highlight the major limitations of existing studies and propose potential research challenges and directions for the development of next generation of sustainable AI techniques.
arXiv Detail & Related papers (2022-05-08T09:38:35Z) - Towards technological adaptation of advanced farming through AI, IoT,
and Robotics: A Comprehensive overview [0.0]
Artificial Intelligence (AI), Internet of Things (IoT), and Robotics-based devices and methods have produced new paradigms and opportunities in agriculture.
The major existing applications of agricultural robotics are for the function of soil preparation, planting, monitoring, harvesting, and storage.
arXiv Detail & Related papers (2022-02-21T07:47:43Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.