Writer: Ecrin Tekes
Introduction
Artificial intelligence (AI) can be challenging to define with precision. Alan Turing, in his groundbreaking paper Computing Machinery and Intelligence, introduced the well-known Turing Test, which measures a machine’s intelligence by determining if its conversation is indistinguishable from that of a human. Today, the term “artificial general intelligence” (AGI) refers to machines that can communicate, reason, and act independently in both familiar and unfamiliar situations, much like humans do. However, this level of intelligence remains beyond our current capabilities. When AI is mentioned in most discussions today, it typically refers to machine learning or deep learning. These are forms of AI where algorithms and statistical models are trained using data to recognize patterns and make predictions, an area particularly relevant in modern technological applications (Du‐Harpur et al, 2020).
Addressing the challenge of feeding an estimated 9 to 10 billion people by 2050 requires a multifaceted approach. The primary strategies include closing the yield gap, enhancing the productivity of crops through technological advancements, reducing food waste, shifting diets, and expanding aquaculture. These efforts must be coordinated to form a comprehensive global strategy aimed at achieving sustainable and equitable food security. Moreover, increasing food production must be coupled with efforts to minimize its environmental impact, particularly about climate change.
In addition to reducing greenhouse gas emissions, we need to focus on preserving freshwater resources and biodiversity, as well as moving toward healthier diets. The expansion of agriculture into natural ecosystems has historically led to significant loss of biodiversity and ecosystem services. Going forward, increasing food production without expanding agricultural land is crucial. This can be achieved through sustainable intensification practices, such as improving crop yields and optimizing land use (Smith, P. and Gregory, 2013).
Sustainable Artificial Intelligence
The term “Sustainable AI” is still in its infancy and represents one of the first academic efforts to define and advocate for its significance. It refers to a research area that addresses both the application of AI technologies in sustainability and the sustainability of AI itself throughout its lifecycle—from design to deployment. This concept encompasses two key distinctions: “AI for sustainability,” which focuses on leveraging AI to achieve goals like the United Nations Sustainable Development Goals, and “sustainability of AI,” which assesses the environmental impacts associated with AI development and usage.
AI for sustainability explores how technologies like machine learning can contribute to affordable energy access and other sustainable outcomes. However, the energy-intensive nature of training AI models raises critical questions about their overall sustainability. Conversely, the sustainability of AI examines how to measure and minimize the environmental footprint of AI systems, including their carbon emissions and energy consumption.
Sustainable development is often described as meeting the needs of the present without compromising the ability of future generations to meet their own needs. In this light, Sustainable AI must balance innovation with environmental responsibility, ensuring that the development of AI technologies does not harm ecological systems or societal values. The environmental costs of AI training, particularly in natural language processing, highlight the necessity for policymakers to consider the broader implications of AI applications, including their energy demands and emissions.
Ultimately, for AI to effectively empower society, its development and deployment must adhere to sustainable principles. This requires conscious choices from all stakeholders to uphold values such as privacy, fairness, and justice, ensuring that the benefits of AI do not come at an unsustainable cost (Van Wynsberghe, 2021).
Artificial Intelligence for Sustainable Food Production
The European Green Deal communication sets a transformative path for the European economy and society. It is intrinsically linked to the United Nations’ 2030 Agenda and Sustainable Development Goals, as well as the priorities outlined in President von der Leyen’s political guidelines. This policy aims to maximize benefits for health, quality of life, resilience, and competitiveness. A key element in this framework is the commitment to sustainable practices in food production.
Food is produced within a complex network of interconnected supply chain actors globally. The performance of this food system is influenced by various interrelated international and local developments. Numerous drivers affecting the food supply chain have been identified, including (i) the growing global population and changing dietary patterns, (ii) resource scarcity necessary for agricultural production (such as fertile land, freshwater, and energy), (iii) climate change, and (iv) declining agricultural productivity.
The Netherlands exemplifies high productivity and resource use efficiency in agriculture. This success results from favorable production conditions, craftsmanship, the adoption of advanced technologies, and well-established agricultural supply chains. Despite significant differences across farms and stages of the supply chain, the common goal is to produce more and better with less, while addressing climate change and reducing pesticide use.
Artificial intelligence and digitalization demonstrate substantial potential to support the transition to sustainable food systems that balance societal needs. This development will influence the roles and interactions of actors across the value chain, from farmers to consumers. The adoption and integration of these technologies will vary by sector and region. As data production increases, issues of data ownership, privacy, and security will also need to be addressed (Marvin, H. J. et al, 2022).
Conclusion
In summary, the integration of artificial intelligence (AI) in agriculture presents a transformative opportunity to enhance sustainable food production in the face of significant global challenges. As the world approaches a population of 9 to 10 billion by 2050, leveraging AI technologies becomes crucial for improving crop yields, optimizing resource use, and reducing environmental impacts. The concept of Sustainable AI emphasizes the dual responsibility of utilizing AI to promote sustainability while ensuring that its development does not compromise ecological integrity.
The European Green Deal and its alignment with the United Nations Sustainable Development Goals underscore the necessity for a holistic approach to food systems that prioritizes health, resilience, and competitiveness. Countries like the Netherlands exemplify how advanced technologies can lead to efficient agricultural practices that are essential for meeting future food demands sustainably.
However, as we embrace AI in agriculture, it is vital to address the associated ethical and environmental considerations, including data privacy and the carbon footprint of AI systems. By committing to these sustainable principles, we can harness the full potential of AI to create a more resilient and equitable food system for future generations.
References:
Du‐Harpur, X., Watt, FM, Luscombe, NM, & Lynch, MD (2020). What is artificial intelligence? Applications of artificial intelligence to dermatology. British Journal of Dermatology, 183 (3), 423-430.
Smith, P. ve Gregory, PJ (2013). Climate change and sustainable food production. Proceedings of the nutrition community, 72 (1), 21-28.
Marvin, H. J., Bouzembrak, Y., Van der Fels-Klerx, H. J., Kempenaar, C., Veerkamp, R., Chauhan, A., ... & Tekinerdogan, B. (2022). Digitalisation and Artificial Intelligence for Sustainable Food Systems. Trends in Food Science & Technology, 120, 344-348.
Van Wynsberghe, A. (2021). Sustainable AI: AI for sustainability and the sustainability of AI. AI and Ethics, 1(3), 213-218.
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