With increasing automation, the ‘human’ element in industrial systems is gradually being reduced, often for the sake of standardization. Complete automation, however, might not be optimal in complex, uncertain environments due to the dynamic and unstructured nature of interactions. Leveraging human perception and cognition can prove fruitful in making automated systems robust and sustainable. “Human-in-the-loop” (HITL) systems are systems which incorporate meaningful human interactions into the workflow. Agricultural Robotic Systems (ARS), developed for the timely detection and prevention of diseases in agricultural crops, are an example of cyber-physical systems where HITL augmentation can provide improved detection capabilities and system performance. Humans can apply their domain knowledge and diagnostic skills to fill in the knowledge gaps present in agricultural robotics and make them more resilient to variability. Owing to the multi-agent nature of ARS, HUB-CI, a collaborative platform for the optimization of interactions between agents is emulated to direct workflow logic. The challenge remains in designing and integrating human roles and tasks in the automated loop. This article explains the development of a HITL simulation for ARS, by first realistically modeling human agents, and exploring two different modes by which they can be integrated into the loop: Sequential, and Shared Integration. System performance metrics such as costs, number of tasks, and classification accuracy are measured and compared for different collaboration protocols. The results show the statistically significant advantages of HUB-CI protocols over the traditional protocols for each integration, while also discussing the competitive factors of both integration modes. Strengthening human modeling and expanding the range of human activities within the loop can help improve the practicality and accuracy of the simulation in replicating a HITL-ARS.

中文翻译：

---

人在环：在网络物理农业系统中的作用

随着自动化程度的提高，通常出于标准化的目的，工业系统中的“人”元素逐渐减少。但是，由于交互的动态和非结构化性质，在复杂，不确定的环境中，完全自动化可能不是最佳选择。利用人类的感知和认知可以使自动化系统变得强大和可持续。“人在环”（HITL）系统是将有意义的人机交互纳入工作流程的系统。开发用于及时检测和预防农作物疾病的农业机器人系统（ARS）是网络物理系统的一个示例，其中HITL增强可以提供改进的检测能力和系统性能。人类可以运用他们的领域知识和诊断技能来填补农业机器人技术中存在的知识空白，并使它们对变化的适应性更强。由于ARS的多代理性质，HUB-CI被仿真为优化代理之间的交互的协作平台，以指导工作流程逻辑。挑战仍然在于在自动化循环中设计和集成人员角色和任务。本文通过首先对人类行为主体进行现实建模，并探索两种可以将其集成到循环中的不同模式来说明针对ARS的HITL仿真的开发：顺序和共享集成。测量并比较了不同协作协议的系统性能指标，例如成本，任务数量和分类准确性。结果表明，对于每个集成，HUB-CI协议相对于传统协议在统计上具有显着优势，同时还讨论了两种集成模式的竞争因素。加强人员建模并扩大回路中人员活动的范围可以帮助提高复制HITL-ARS时仿真的实用性和准确性。