The role of generative artificial intelligence in digital agri-food

The agriculture and food (agri-food) sector faces rising global concerns about its sustainability and resilience to climate events. Thus, new solutions are needed to ensure environmental and food security. Artificial Intelligence (AI) offers inventive solutions to improve agricultural and food production practices. Generative AI methods, such as generative adversarial networks (GANs), variational autoencoders, and large language models (LLMs), add to the transformative process initiated by AI and expert systems in agricultural and food-related practices to enhance productivity, sustainability, and resilience. This study categorizes generative AI approaches and their capabilities in agri-food systems and provides a comprehensive review of the current landscape of generative AI applications in the sector. It discusses the impact of these technologies on enhancing agricultural productivity, food quality, and safety, as well as sustainability, presenting potential use cases like combatting climate change and foodborne disease modeling that highlight the practical applications and benefits of generative AI in agri-food. Furthermore, it addresses the ethical implications of deploying generative AI, including privacy, security, reliability, and unbiased decision-making. • Generative AI enhances agri-food systems through predictive analytics, food design, and sustainability. • Applications include disease modeling, supply chain optimization, and combating climate change. • Emerging technologies like transformers and large language models drive innovation in agriculture and food production. • Ethical considerations address transparency, bias, and privacy in AI-driven agri-food solutions. • Practical insights bridge AI advancements with agri-food challenges for researchers and policymakers.