构建可持续的农田数据生态，激励农民持续提供真实数据。
Conceptual Design of an Ecosystem for Real Farm Data Collection toward Agricultural AI Foundation Models
- 按需求与稀缺性自动定价，驱动数据流通
- 农民获收益分成，长期参与意愿提升
- 设备认证上传确保数据真实可信
农业机器人领域面临数据稀缺的根本挑战。现有开源数据平台缺乏对数据提供者的足够激励，导致长期数据收集困难。同时，生成式AI的发展带来了验证数据是否源自真实农场环境的新难题。本文提出一个可持续的农田数据收集与分发生态系统，整合基于需求与稀缺性的自动定价机制、向农民分配收益的分成模式，以及通过设备认证上传实现的数据真实性保障。为验证平台、农业公司和农民三方的经济可持续性，我们估算农业机器人可能产生的经济价值。
原文摘要 · Abstract (English)
Data scarcity is a fundamental challenge in developing AI and foundation models for agricultural robots. Existing open-source data platforms do not provide sufficient incentives for data providers so long-term data collection remains difficult. Furthermore, advances in generative AI have introduced a new challenge of verifying that collected data genuinely originates from real farm environments. We propose an ecosystem for the sustainable collection and distribution of real farm data, integrating automatic pricing driven by demand and rarity, revenue sharing that distributes earnings to farmers as an incentive to keep providing data, and data authenticity guarantees through authenticated device uploads. To demonstrate the economic sustainability for all three parties among farmers, AI companies, and the platform, we estimate the economic value that agricultural robots stand to generate.
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