[Paper Review] From Plate to Production: Artificial Intelligence in Modern Consumer-Driven Food Systems

Weiqing Min, Pengfei Zhou+9 more|arXiv (Cornell University)|Nov 4, 2023

Culinary Culture and Tourism11 citations

TL;DR

The paper proposes and surveys AI-enabled Food Systems (AIFS), a consumer-driven framework that integrates AI with technologies like IoT and big data to transform food systems from plate to production and back, aiming for sustainable healthy diets.

ABSTRACT

Global food systems confront the urgent challenge of supplying sustainable, nutritious diets in the face of escalating demands. The advent of Artificial Intelligence (AI) is bringing in a personal choice revolution, wherein AI-driven individual decisions transform food systems from dinner tables, to the farms, and back to our plates. In this context, AI algorithms refine personal dietary choices, subsequently shaping agricultural outputs, and promoting an optimized feedback loop from consumption to cultivation. Initially, we delve into AI tools and techniques spanning the food supply chain, and subsequently assess how AI subfields$\unicode{x2013}$encompassing machine learning, computer vision, and speech recognition$\unicode{x2013}$are harnessed within the AI-enabled Food System (AIFS) framework, which increasingly leverages Internet of Things, multimodal sensors and real-time data exchange. We spotlight the AIFS framework, emphasizing its fusion of AI with technologies such as digitalization, big data analytics, biotechnology, and IoT extensively used in modern food systems in every component. This paradigm shifts the conventional "farm to fork" narrative to a cyclical "consumer-driven farm to fork" model for better achieving sustainable, nutritious diets. This paper explores AI's promise and the intrinsic challenges it poses within the food domain. By championing stringent AI governance, uniform data architectures, and cross-disciplinary partnerships, we argue that AI, when synergized with consumer-centric strategies, holds the potential to steer food systems toward a sustainable trajectory. We furnish a comprehensive survey for the state-of-the-art in diverse facets of food systems, subsequently pinpointing gaps and advocating for the judicious and efficacious deployment of emergent AI methodologies.

Motivation & Objective

• Introduce the AI-enabled Food Systems (AIFS) framework and its consumer-driven perspective.
• Survey state-of-the-art AI tools and techniques across the food supply chain.
• Analyze how AI can shift decisions from consumption to production and back to plate to promote sustainability and health.
• Identify gaps, governance needs, and pathways for practical deployment of emergent AI methods in food systems.

Proposed method

• Survey AI core techniques (ML, DL, CV, NLP, speech) and their roles in food systems.
• Describe the AIFS framework and its integration with IoT, big data, robotics, blockchain, biotechnology.
• Map AI-enabled tasks across stages from consuming to production, processing, transport, and disposal.
• Discuss governance, data architectures, and cross-disciplinary collaborations required for deployment.
• Synthesize state-of-the-art findings and outline gaps and future research directions.

Experimental results

Research questions

• RQ1 What is the AI-enabled Food Systems (AIFS) framework and how does it differ from traditional 'farm to fork' models?
• RQ2 How can AI, in combination with IoT and other technologies, improve the availability, accessibility, affordability, and desirability of nutritious foods within consumer-driven systems?
• RQ3 What are the main challenges, governance needs, and data-standard requirements for deploying AI in food systems?
• RQ4 Where are the gaps in current research, and what future directions does the survey identify for effective, sustainable AI deployment?

Key findings

• AI can shape consumer choices and feedback loops to influence upstream agricultural production.
• The AIFS framework integrates AI with IoT, big data, robotics, cloud computing, blockchain, and nanotechnology across the food system.
• A consumer-centric shift from plate to production requires governance, standardized data architectures, and cross-disciplinary cooperation.
• The survey outlines state-of-the-art AI applications across consumption, production, processing, transport, and disposal, and highlights gaps and future research needs.
• AI promises to advance sustainable healthy diets but must be implemented with attention to environmental footprint and governance constraints.