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.
翻译:在农业产业中应用互联网物质(IoT)和机器学习(ML)使智能农场和精密农业得以发展和创建,智能农场数量的增长和这些农场之间的潜在合作产生了合作智能农场(CSF),在不同连接农场相互合作并共享数据以互利互利的情况下,通过CSF共享数据具有各种优势,不同农场的个人数据可以由ML模型汇总,并用于产生可操作的产出,然后由CSF中的所有农场使用。这使得农场能够更好地了解提高预期产出的公正性,如作物产量、管理水资源和灌溉时间表以及更好的种子应用。然而,当一些农场不转让高质量数据,而是依赖其他农场来喂养ML模型时,CSF可能会出现复杂情况。另外一种可能性是,CSF中存在无赖农场的个人数据,这些农场在不提供任何数据的情况下,我们分析使用游戏理论方法解决CSF的农场的行为,而每个农场的动机是最大限度地增加其利润、管理水资源和灌溉时间表,以及更好的种子应用。因此,CSFFA没有提供更好的数据,因此,我们没有提供更好的数据库。