Autonomous Fruit Harvester with Machine Vision

Authors

  • Kathleen Anne M. Almendral Electronics and Communications Engineering Department of De La Salle University, Manila, 2401 Taft Ave., Malate, Manila 1004, Philippines.
  • Rona Mae G. Babaran Electronics and Communications Engineering Department of De La Salle University, Manila, 2401 Taft Ave., Malate, Manila 1004, Philippines.
  • Bryan Jones C. Carzon Electronics and Communications Engineering Department of De La Salle University, Manila, 2401 Taft Ave., Malate, Manila 1004, Philippines.
  • Karl Patrick K. Cu Electronics and Communications Engineering Department of De La Salle University, Manila, 2401 Taft Ave., Malate, Manila 1004, Philippines.
  • Jasmine M. Lalanto Electronics and Communications Engineering Department of De La Salle University, Manila, 2401 Taft Ave., Malate, Manila 1004, Philippines.
  • Alexander C. Abad Electronics and Communications Engineering Department of De La Salle University, Manila, 2401 Taft Ave., Malate, Manila 1004, Philippines.

Keywords:

Fruit Harvester, Machine Vision, Robotic Arm, ZED Stereo Camera,

Abstract

This study presents an autonomous fruit harvester with a machine vision capable of detecting and picking or cutting an orange fruit from a tree. The system of is composed of a six-degrees of freedom (6-DOF) robotic arm mounted on a four-wheeled electric kart. The kart uses ZED stereo camera for depth estimation of a target. It can also be used to detect trees using the green detection algorithm. Image processing is done using Microsoft Visual Studio and OpenCV library. The x & y coordinates and distance of the tree are passed on to Arduino microcontroller as inputs to motor control of the wheels. When the kart is less than 65cm to the tree, the kart stops and the robotic arm system takes over to search and harvest orange fruits. The robotic arm has a webcam and ultrasonic sensor attached at its end-effector. The webcam is used for orange fruit detection while ultrasonic sensor is used to provide feedback on the distance of the orange fruit to end-effector. Multiple fruit harvesting is successfully done. The success rate of harvesting and putting fruit into the basket is 80% and 85% for the gripper end-effector and cutter end-effector respectively.

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Published

2018-02-05

How to Cite

Almendral, K. A. M., Babaran, R. M. G., Carzon, B. J. C., Cu, K. P. K., Lalanto, J. M., & Abad, A. C. (2018). Autonomous Fruit Harvester with Machine Vision. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-6), 79–86. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3671

Issue

Vol. 10 No. 1-6: Breakthrough To Excellence in Communication and Computer Engineering III

Section

Articles

License

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