Publications

@article{ahmed_integrated_2025,

title = {An Integrated Visual Servoing Framework for Precise Robotic Pruning Operations in Modern Commercial Orchard},

author = {Dawood Ahmed and Basit Muhammad Imran and Martin Churuvija and Manoj Karkee},

url = {https://www.sciencedirect.com/science/article/pii/S2405896325024565},

doi = {https://doi.org/10.1016/j.ifacol.2025.11.758},

issn = {2405-8963},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {IFAC-PapersOnLine},

volume = {59},

number = {23},

pages = {28\textendash33},

abstract = {This study presents a vision-guided robotic control system for automated fruit tree pruning applications. Traditional pruning practices are labor-intensive and limit agricultural efficiency and scalability, highlighting the need for advanced automation. A key challenge is the precise, robust positioning of the cutting tool in complex orchard environments, where dense branches and occlusions make target access difficult. To address this, an Intel RealSense D435 camera is mounted on the flange of a UR5e robotic arm and CoTracker3, a transformer-based point tracker, is utilized for visual servoing control that centers tracked points in the camera view. The system integrates proportional control with iterative inverse kinematics to achieve precise end-effector positioning. The system was validated in Gazebo simulation, achieving a 77.77% success rate within 5mm positional tolerance and 100% success rate within 10mm tolerance, with a mean end-effector error of 4.28 ± 1.36 mm. The vision controller demonstrated robust performance across diverse target positions within the pixel workspace. The results validate the effectiveness of integrating vision-based tracking with kinematic control for precision agricultural tasks. Future work will focus on real-world implementation and the integration of force sensing for actual cutting operations.},

keywords = {agricultural automation, agricultural robotics, perception, precision pruning, robotic manipulator, sensing, visual servoing},

pubstate = {published},

tppubtype = {article}

}

@article{bhattarai_design_2024,

title = {Design, integration, and field evaluation of a robotic blossom thinning system for tree fruit crops},

author = {Uddhav Bhattarai and Qin Zhang and Manoj Karkee},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.22330},

doi = {10.1002/rob.22330},

issn = {1556-4967},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Journal of Field Robotics},

volume = {41},

number = {5},

pages = {1366\textendash1385},

abstract = {The United States (US) apple industry relies heavily on semi-skilled manual labor force for essential field operations such as training, pruning, blossom and green fruitlet thinning, and harvesting. Blossom thinning is one of the crucial crop-load management practices to achieve desired crop load, fruit quality, and return bloom. While several techniques such as chemical and mechanical thinning are available for large-scale blossom thinning, such approaches often yield unpredictable thinning results and may damage the canopy, spurs, and leaf tissue. Hence, growers still depend on laborious, labor-intensive, and expensive manual hand blossom thinning for desired thinning outcomes. This research presents a robotic solution for precision blossom thinning in apple orchards using a deep learning-based computer vision system, a six-degrees-of-freedom UR5e robotic manipulator, and an electrically actuated miniature end-effector. The integrated robotic system was evaluated in a commercial apple orchard which showed promising results for targeted and selective blossom thinning. Two thinning approaches, center and boundary thinning, were investigated to evaluate the system's ability to remove varying proportions of flowers from apple flower clusters. During boundary thinning, the end-effector was actuated around the cluster boundary, while center thinning involved end-effector actuation only at the cluster centroid for a fixed duration of 2 s. Field evaluation results showed that the boundary thinning approach thinned 67.2% of flowers from the targeted clusters with a cycle time of 9.0 s per cluster, whereas the center thinning approach thinned 59.4% of flowers with a cycle time of 7.2 s per cluster. Upon further improvement for commercial adoption, the proposed system could help address problems faced by apple growers with current hand, chemical, and mechanical blossom thinning approaches.},

note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/rob.22330},

keywords = {agricultural automation, agricultural robotics, artificial intelligence in agriculture, blossom thinning, robotic thinning},

pubstate = {published},

tppubtype = {article}

}