Human-in-the-loop: Role in Cyber Physical Agricultural Systems

Abstract

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.

Robotic and Autonomous Systems (RAS): Dynamic Modeling of Cyber-physical Environments

“…The article presents a novel approach to handling intricate relationships and real-time changes in networked ecosystems: analyzing Nonlinear Systems Cyber-Physical Modelling (ANSC-PM). Through the use of adaptive neural networks and complex mathematical frameworks [15], ANSC-PM can identify and adjust its operation in realtime in response to subtle behaviors.…”

Smart Agriculture in the Digital Age: A Comprehensive IoT-Driven Greenhouse Monitoring System

“…Smart agriculture, while promising numerous benefits, also faces several concerns and challenges that need to be addressed for its widespread adoption and sustainable development. Some of the key concerns in the field of smart agriculture: data security and privacy [22], cost of technology adoption [23], data interoperability, reliability and connectivity [24] [25] [26], energy efficiency [27], environ-Figure 1: Categories of challenges in smart agriculture applications based on IoT mental impact [28], regulatory and legal issues, scalability and standardization [29] [30]. Our research collective has been dealing with this problem for some time, our concerns being more and more directed towards small farmers, basically in the western part of Romania [31][32].…”

Structural Design of a Passive Wearable Exoskeleton to Assist Oil Palm Harvesting Operation

“…Agricultural workers interact with and handle machines and tools of various sizes and shapes to match the characteristics of the crops and the environment. This unique characteristic of agricultural work is a challenge to comprehensive mechanisation and automation in the field (Marinoudi et al, 2019;Sreeram and Nof, 2021;Vasconez et al, 2019). Moreover, the dynamic interactions between the user, task and environment is especially critical for automation.…”