From Simulation to Field Validation: A Digital Twin-Driven Sim2real Transfer Approach for Strawberry Fruit Detection and Sizing

Typically, developing new digital agriculture technologies requires substantial on-site resources and data. However, the crop’s growth cycle provides only limited time windows for experiments and equipment validation. This study presents a photorealistic digital twin of a commercial-scale strawberry farm, coupled with a simulated ground vehicle, to address these constraints by generating high-fidelity synthetic RGB and LiDAR data. These data enable the rapid development and evaluation of a deep learning-based machine vision pipeline for fruit detection and sizing without continuously relying on real-field access. Traditional simulators often lack visual realism, leading many studies to mix real images or adopt domain adaptation methods to address the reality gap. In contrast, this work relies solely on photorealistic simulation outputs for training, eliminating the need for real images or specialized adaptation approaches. After training exclusively on images captured in the virtual environment, the model was tested on a commercial-scale strawberry farm using a physical ground vehicle. Two separate trials with field images resulted in F1-scores of 0.92 and 0.81 for detection and a sizing error of 1.4 mm (R² = 0.92) when comparing image-derived diameters against caliper measurements. These findings indicate that a digital twin-driven sim2real transfer can offer substantial time and cost savings by refining crucial tasks such as stereo sensor calibration and machine learning model development before extensive real-field deployments. In addition, the study examined geometric accuracy and visual fidelity through systematic comparisons of LiDAR and RGB sensor outputs from the virtual and real farms. Results demonstrated close alignment in both topography and textural details, validating the digital twin’s ability to replicate intricate field characteristics, including raised bed geometry and strawberry plant distribution. The techniques developed and validated in this strawberry project have broad applicability across agricultural commodities, particularly for fruit and vegetable production systems. This study demonstrates that integrating digital twins with simulation tools can significantly reduce the need for resource-intensive field data collection while accelerating the development and refinement of agricultural robotics algorithms and hardware.