[论文解读] 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 Tourism被引用 11

一句话总结

本文提出并综述 AI-enabled Food Systems (AIFS)，这是一个以消费者为驱动的框架，将 AI 与物联网 IoT、大数据等技术结合，旨在将食品系统从餐盘到生产再回到餐盘进行转型，以实现可持续健康的饮食。

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.

研究动机与目标

• 介绍 AI-enabled Food Systems (AIFS) 框架及其以消费者为驱动的视角。
• 调研食品供应链中的前沿 AI 工具和技术。
• 分析 AI 如何将决策从消费转向生产再回到餐盘，以促进可持续性与健康。
• 识别食品系统中新兴 AI 方法在实际部署中的差距、治理需求及实施路径。

提出的方法

• 调查 AI 的核心技术（ML、DL、CV、NLP、speech）及它们在食品系统中的作用。
• 描述 AIFS 框架及其与 IoT、大数据、机器人技术、区块链、生物技术的整合。
• 绘制从消费到生产、加工、运输与处置各阶段的 AI-enabled 任务映射。
• 讨论部署所需的治理、数据架构与跨学科协作。
• 综合前沿研究发现，概述差距与未来研究方向。

实验结果

研究问题

• RQ1 AI-enabled Food Systems (AIFS) 框架是什么，它与传统的‘farm to fork’模型有何不同？
• RQ2 在以消费者为驱动的系统中，AI 结合 IoT 等技术如何改善营养食品的可得性、可获取性、可负担性与吸引力？
• RQ3 在食品系统部署 AI 时的主要挑战、治理需求与数据标准要求是什么？
• RQ4 当前研究中有哪些空白，综述所识别的面向高效、可持续 AI 部署的未来方向是什么？

主要发现

• AI 可以塑造消费者选择与反馈环，从而影响上游农业生产。
• AIFS 框架在整个食品系统中将 AI 与 IoT、大数据、机器人技术、云计算、区块链和纳米技术整合。
• 以消费者为中心的从餐盘到生产的转变需要治理、标准化数据架构以及跨学科合作。
• 综述概述了在消费、生产、加工、运输与处置等环节的前沿 AI 应用，并强调了差距与未来研究需求。
• AI 有望推动可持续健康饮食，但必须在关注环境足迹与治理约束的前提下实施。