合作智慧农业的博弈分析
A Game Theoretic Analysis for Cooperative Smart Farming
摘要：
物联网（IoT）和机器学习（ML）在农业领域的应用，促进了智慧农场和精准农业的发展和创造。智能农场数量的增长以及这些农场之间的潜在合作催生了合作智能农业（CSF），在CSF中，不同的互联农场相互合作并共享数据，以实现互利。通过CSF的这种数据共享具有各种优势，其中来自不同农场的个体数据可以通过ML模型聚合，并用于产生可操作的输出，然后可以由CSF中的所有农场使用。这使农场能够获得更好的见解，以提高所需的产出，如作物产量，管理水资源和灌溉计划，以及更好的种子应用。然而，当一些农场没有传输高质量的数据，而是依赖其他农场来喂养ML模型时，CSF可能会出现并发症。另一种可能性是CSF中存在流氓农场，这些农场希望窥探其他农场，但实际上没有贡献任何数据。在本文中，我们分析了农场参与CSF的行为，采用博弈论的方法，其中每个农场的动机是最大化其利润。我们首先提出了由于缺乏更好的数据而导致CSF中存在缺陷农场的问题，然后提出了一个ML框架，该框架可以隔离农场，并根据它们提供的数据质量自动将它们分配到适当的CSF集群。我们提出的模型奖励提供更好数据的农场，惩罚那些不提供所需数据或恶意的农场，从而确保模型的完整性和更好的性能，同时解决有缺陷的农场问题。
英文摘要：
The application of Internet of Things (IoT) and Machine Learning (ML) to the agricultural industry has enabled the development and creation of smart farms and precision agriculture. The growth in the number of smart farms and potential cooperation between these farms has given rise to the Cooperative Smart Farming (CSF) where different connected farms collaborate with each other and share data for their mutual benefit. This data sharing through CSF has various advantages where individual data from separate farms can be aggregated by ML models and be used to produce actionable outputs which then can be utilized by all the farms in CSFs. This enables farms to gain better insights for enhancing desired outputs, such as crop yield, managing water resources and irrigation schedules, as well as better seed applications. However, complications may arise in CSF when some of the farms do not transfer high-quality data and rather rely on other farms to feed ML models. Another possibility is the presence of rogue farms in CSFs that want to snoop on other farms without actually contributing any data. In this paper, we analyze the behavior of farms participating in CSFs using game theory approach, where each farm is motivated to maximize its profit. We first present the problem of defective farms in CSFs due to lack of better data, and then propose a ML framework that segregates farms and automatically assign them to an appropriate CSF cluster based on the quality of data they provide. Our proposed model rewards the farms supplying better data and penalize the ones that do not provide required data or are malicious in nature, thus, ensuring the model integrity and better performance all over while solving the defective farms problem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
