The immense growth of the cloud infrastructure leads to the deployment of several machine learning as a service (MLaaS) in which the training and the development of machine learning models are ultimately performed in the cloud providers’ environment. However, this could also cause potential security threats and privacy risk as the deep learning algorithms need to access generated data collection, which lacks security in nature. This paper predominately focuses on developing a secure deep learning system design with the threat analysis involved within the smart farming technologies as they are acquiring more attention towards the global food supply needs with their intensifying demands. Smart farming is known to be a combination of data-driven technology and agricultural applications that helps in yielding quality food products with the enhancing crop yield. Nowadays, many use cases had been developed by executing smart farming paradigm and promote high impacts on the agricultural lands.

云基础设施的巨大增长导致部署了几种机器学习即服务（MLaaS），其中机器学习模型的培训和开发最终在云提供商的环境中进行。但是，由于深度学习算法需要访问生成的数据集，因此这也可能导致潜在的安全威胁和隐私风险，而这本质上是缺乏安全性的。本文主要关注于开发安全的深度学习系统设计，其中涉及智能农业技术中涉及的威胁分析，因为随着其不断增长的需求，它们越来越关注全球食品供应需求。众所周知，智能农业是数据驱动技术与农业应用的结合，有助于通过提高农作物产量来生产优质食品。如今，通过执行智能农业范式已经开发出许多用例，并促进了对农业土地的巨大影响。