Andrew K. Chesang

Teleoperation in agricultural environments requires enhanced situational awareness for effective architectural scouting and decision-making for orchard management applications. The dynamic complexity of orchard structures presents challenges for remote visualization during architectural scouting operations. This study presents an adaptive streaming and rendering pipeline for real-time point cloud visualization in Virtual Reality (VR) teleoperation systems. The proposed method integrates selective streaming that localizes teleoperators within live maps, an efficient point cloud parser for Unity Engine, and an adaptive Level-of-Detail rendering system utilizing dynamically scaled and smoothed polygons. The implementation incorporates pseudo-coloring through LiDAR reflectivity fields to enhance the distinction between materials and geometry. The pipeline was evaluated using datasets containing LiDAR point cloud scans of orchard environments captured during spring and summer seasons, with testing conducted on both standalone and PC-tethered VR configurations. Performance analysis demonstrated improvements of 10.2–19.4% in runtime performance compared to existing methods, with a framerate enhancement of up to 112% achieved through selectively streamed representations. Qualitative assessment confirms the method’s capability to maintain visual continuity at close proximity while preserving the geometric features discernible for architectural scouting operations. The results establish the viability of VR-based teleoperation for precision agriculture applications, while demonstrating the critical relationship between Quality-of-Service parameters and operator Quality of Experience in remote environmental perception.

Digital twins, defined as virtual counterparts of physical systems that evolve with sensor data have potential applications in controlled-environment agriculture. This study previews the integration of adaptive Microclimate Monitoring within a Unity-based digital twin of a strawberry greenhouse to support dynamic sensor selection and reallocation. Using data collected from 56 distributed temperature–relative humidity sensors, a Thompson Sampling algorithm was deployed to assign monthly importance rankings and identify season-specific subsets of sensors. To evaluate how well these subsets represented the whole sensor network, we used the Z-index, which measures distributional consistency. Across all observed months, Z-index values remained close to zero, with values of 0.037 in February, 0.012 in April, −0.002 in June, and 0.025 in October for relative humidity. These results indicate that the digital twin framework sustains the overall climate trend while reducing sensing redundancy, pointing to its potential role in future climate monitoring strategies within greenhouse systems.