ASABE Technical Library - Abstract

Click on “Download PDF” for the PDF version or on the title for the HTML version.
If you are not an ASABE member or if your employer has not arranged for access to the full-text, view access and subscription options

Beyond Seasonality: A High-Fidelity Apple Orchard Digital Twin

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  2026 ASABE Annual International Meeting  2600941.(doi:10.13031/aim.202600941)
Authors:   Ruiming Du, Yiyuan Lin, Daoyuan Jin, Nicholas Gunner, Yu Jiang
Keywords:   Digital twin; Sim-to-real; Synthetic data generation; Robotics; Perception

Abstract. Agricultural robot development is strongly constrained by seasonality, which limits the efficiency and effectiveness from prototyping to field deployment. For example, robotic pruning must be evaluated during the dormant season, when snow and low temperatures create harsh conditions that are impractical for sustained field experimentation. To address comparable constraints in extreme and inaccessible environments, the concept of a digital twin has been adopted by NASA for spacecraft development, enabling validation without continuous reliance on real-world operations. In agriculture, however, existing digital twins, particularly those targeting complex orchard systems, are limited in availability and often exhibit low fidelity, creating a major bottleneck for simulation-to-real (Sim2Real) transfer learning and learning-based robot control. To address this gap, we developed a high-fidelity digital twin of a modern high-density orchard system using NVIDIA Isaac Sim. The digital twin includes a simulated orchard field, dormant apple trees generated by L-TreeGen with high realism, and a field robot based on an Amiga platform, along with RGB-D and LiDAR sensors for 2D and 3D perception tasks. The system enabled configurable downstream tasks for perception model development, including field synthetic data collection and multiple sim2real learning applications. We evaluated Sim2Real perception on real orchard data at Cornell campus using 2D and 3D pipelines trained on synthetic data from the proposed digital twin: 2D tree semantic segmentation from synthetic RGB-D images and 3D branch instance segmentation with synthetic 3D point clouds. Results showed that models trained on synthetic data consistently improved real-world perception performance, demonstrating the efficacy of the proposed digital twin in facilitating agricultural robot development.

(Download PDF)    (Export to EndNotes)