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Pruning End-Effectors State of the Art Review

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Progress in Artificial Intelligence (EPIA 2024)

Abstract

Pruning consists on an agricultural trimming procedure that is crucial in some species of plants to promote healthy growth and increased yield. Generally, this task is done through manual labour, which is costly, physically demanding, and potentially dangerous for the worker. Robotic pruning is an automated alternative approach to manual labour on this task. This approach focuses on selective pruning and requires the existence of an end-effector capable of detecting and cutting the correct point on the branch to achieve efficient pruning. This paper reviews and analyses different end-effectors used in robotic pruning, which helped to understand the advantages and limitations of the different techniques used and, subsequently, clarified the work required to enable autonomous pruning.

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Notes

  1. 1.

    Universal Robots UR5e, https://www.universal-robots.com/products/ur5-robot/.

  2. 2.

    Intel RealSense D345, https://www.intelrealsense.com/depth-camera-d435/.

  3. 3.

    Arduino Mega 2560, https://store.arduino.cc/collections/boards-modules/products/arduino-mega-2560-rev3.

  4. 4.

    Mathworks Matlab, https://www.mathworks.com/products/matlab.html.

  5. 5.

    Kinova Robotics, https://www.kinovarobotics.com/.

  6. 6.

    Maxon, https://www.maxongroup.com/en.

  7. 7.

    FarmBot, https://farm.bot/.

  8. 8.

    Betu Servo, https://betuservo.com/.

  9. 9.

    Intel RealSense T265, https://www.intel.com/content/www/us/en/products/sku/192742/intel-realsense-tracking-camera-t265/specifications.html.

  10. 10.

    Makita, https://www.makita.biz/.

  11. 11.

    Ricoh Theta V, https://us.ricoh-imaging.com/product/theta-v/.

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Acknowledgments

This work is co-financed by Component 5 - Capitalization and Business Innovation, integrated in the Resilience Dimension of the Recovery and Resilience Plan within the scope of the Recovery and Resilience Mechanism (MRR) of the European Union (EU), framed in the Next Generation EU, for the period 2021–2026, within project Vine&Wine_PT, with reference 67.

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Correspondence to Francisco Oliveira .

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Oliveira, F., Tinoco, V., Valente, A., Pinho, T., Cunha, J.B., Santos, F.N. (2025). Pruning End-Effectors State of the Art Review. In: Santos, M.F., Machado, J., Novais, P., Cortez, P., Moreira, P.M. (eds) Progress in Artificial Intelligence. EPIA 2024. Lecture Notes in Computer Science(), vol 14967. Springer, Cham. https://doi.org/10.1007/978-3-031-73497-7_14

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  • DOI: https://doi.org/10.1007/978-3-031-73497-7_14

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