This paper presents a novel approach for predicting the simulation-to-real-world (sim2real) transferability of synthetic-trained vision models by analyzing foreground image quality. Image quality assessment (IQA) techniques are applied to paired synthetic and real-world image subsets to quantify visual similarity and identify feature-level limitations that correlate with transfer performance. Among the evaluated metrics, the Structural Similarity (SSIM) index and Complex Wavelet SSIM (CW-SSIM) index showed consistent trends, where higher IQA scores corresponded to improved sim2real detection accuracy–most notably with increases of approximately 0.10 (SSIM) and 0.15 (CW-SSIM). These metrics effectively captured contrast, structure, and luminance similarities, offering a practical proxy for assessing digital twin fidelity. To support reproducibility and broader use, we introduce the Synthetic Image Quality Analysis Calculator (SIQAC), an open-source tool for automated IQA evaluation and sim2real potential prediction across classifiers and object detectors. Additional experiments demonstrated that the IQA-based approach generalizes to real2sim scenarios using zero-shot object detectors. This work bridges concepts from the human visual system and compression analysis to provide a lightweight, interpretable method for early-stage validation of virtual autonomy pipelines.

Monitoring growth behavior of maize plants such as the development of ears can give key insights into the plant's health and development. Traditionally, the measurement of the angle of ears is performed manually, which can be time-consuming and prone to human error. To address these challenges, this paper presents a computer vision-based system for detecting and tracking ears of corn in an image sequence. The proposed system could accurately detect, track, and predict the ear's orientation, which can be useful in monitoring their growth behavior. This can significantly save time compared to manual measurement and enables additional areas of ear orientation research and potential increase in efficiencies for maize production. Using an object detector with keypoint detection, the algorithm proposed could detect 90 percent of all ears. The cardinal estimation had a mean absolute error (MAE) of 18 degrees, compared to a mean 15 degree difference between two people measuring by hand. These results demonstrate the feasibility of using computer vision techniques for monitoring maize growth and can lead to further research in this area.

Roadside mowing is a necessary, high-risk occupation to ensure visibility for on-road traffic. In Indiana, contractors are hired by the Indiana Department of Transportation (INDOT) to mow over 11,000 miles of roadsides using tractors with flex-wing mowers and hand-held string trimmers. Operations have significant risks to all crew members. Autonomous mowers have gained interest as a potential solution to reduce the risk. INDOT has expressed interest in how to evaluate the technological readiness of autonomous mowers. This work developed ROWMow Sim, a digital-twin autonomous mowing simulator that can test and evaluate roadside mowing operations. ROWMow Sim generates virtual environments of real-world roadways, providing an informative testbed with zero risk. Virtual models of sensors and vehicles were developed to be tested in virtual roadways prior to real-world deployment. ROWMow Sim provides rapid creation of various environment conditions, repeatable tests, ground truth data analysis, mower configuration studies, and direct simulation-to-real-world transfers. Autonomous navigation strategies developed on a virtual string trimming vehicle were transferred directly to the physical vehicle. ROWMow Sim contributes to the exploration of safer and more efficient vegetation management operations for INDOT and the on-road traffic.

In Indiana, roadside mowing operations span over 11,000 miles and are performed by contracted crews using tractors and hand-held trimmers, exposing workers to significant safety risks, including reported casualties. Autonomous mowers offer a promising solution to enhance safety, but large-scale deployment requires rigorous testing to ensure reliability. This study addresses this need by developing digital-twin roadside environments to evaluate autonomous mowing systems. The method generates realistic roadways that accurately replicate real-world conditions, achieving single-float rounding planar accuracy over tens of meters, enabling risk-free and comprehensive testing. Leveraging remote sensing data, model libraries, and a georeferenced data mapping tool, the approach overcomes challenges in scalability and fidelity, even in locations with low-density source data. Digital roadway construction accelerates testing, enhances the realism of virtual deployments, and supports the development of autonomous mowing technologies. Furthermore, this approach has broader applications for generating test environments for various roadway-related systems.

Purpose Accurate measurement of corn stalk diameter is important for assessing plant robustness, lodging resistance, and harvest performance, but automated measurement across changing crop conditions remains challenging. This study evaluated whether a ground-based stereo-vision system could reliably estimate stalk diameter throughout the growing season, from mid-summer through senescence. Methods A stereo-vision pipeline using dual AR0234 global-shutter cameras was deployed on an autonomous ground robot. YOLOv8 provided stalk localization and pose correction, BoT-SORT enabled multi-frame tracking, and U-Net segmentation with an edge-mask strategy extracted stalk boundaries under partial occlusion. Repeated per-frame width estimates were filtered and converted to physical dimensions using disparity-based calibration. Vision estimates were compared with perpendicular caliper measurements. Results Under mid-summer conditions with minimal leaf interference, the system achieved a mean absolute error (MAE) of 1.1–1.5 mm and r² of 0.90. During late-season senescence, leaf-sheath expansion and occlusion caused systematic diameter overestimation and reduced accuracy. Applying a seasonally derived offset of approximately 3.8 mm reduced MAE to approximately 1.3 mm, although correlation remained modest (r² ≈ 0.41). Independent human measurements also exhibited variability, with inter-rater r² ≈ 0.77 and mean disagreement of approximately 1.0 mm. Conclusion Stereo vision can provide accurate, non-contact corn stalk diameter measurements under favorable canopy conditions and remains viable across the crop lifecycle. However, robust late-season phenotyping will require improved modeling of leaf sheaths and occlusions together with more reliable ground-truth measurement procedures.

This paper presents a novel approach for predicting the simulation-to-real-world (sim2real) transferability of synthetic-trained vision models by analyzing foreground image quality. Image quality assessment (IQA) techniques are applied to paired synthetic and real-world image subsets to quantify visual similarity and identify feature-level limitations that correlate with transfer performance. Among the evaluated metrics, the Structural Similarity Index (SSIM) and Complex Wavelet SSIM (CW-SSIM) showed consistent trends, where higher IQA scores corresponded to improved sim2real detection accuracy—most notably with increases of approximately 0.10 (SSIM) and 0.15 (CW-SSIM). These metrics effectively captured contrast, structure, and luminance similarities, offering a practical proxy for assessing digital twin fidelity. To support reproducibility and broader use, we introduce the Synthetic Image Quality Analysis Calculator (SIQAC), an open-source tool for automated IQA evaluation and sim2real potential prediction across classifiers and object detectors. Additional experiments demonstrated that the IQA-based approach generalizes to real2sim scenarios using zero-shot object detectors. This work bridges concepts from the human visual system and compression analysis to provide a lightweight, interpretable method for early-stage validation of virtual autonomy pipelines.