Detection of clustered and occluded oranges from a color image of an orange tree


The environment in which robotic fruit harv_e~ters work presents many challenges. Developers of a system that aims at performing in this environment need to take into account its inherent variability and devise their system to be robust enough to perform acceptably in all cases. This is no easy task, and a lot of work remains to be completed before the perfect fruit-harvesting robot is designed. This thesis attempts to take a step towards this goal by outlining an algorithm that would allow a vision-controlled robotic harvester to detect and accurately locate oranges in the canopy of an orange tree_ Most of the other researchers that have addressed this issue did not tackle the problem of accurately locating the oranges in an image of an orange tree, only differentiating the orange regions from the rest of the picture. In a previous work at UCF by Joakim Eriksson a system was developed that accurately locates the single, non occluded oranges in a color image of an orange tree. In this thesis, this initial work will be improved upon to give the system the capability of finding occluded oranges, and oranges inside a cluster increasing the overall detection accuracy at locating oranges. Accurately pinpointing the location of the orange would allow a robotic harvester to cut the stem of the orange by either scanning the top of the orange with a laser or by directing a robotic arm towards the stem to manually cut it. Future work needs to address the collection of the oranges after the robot has harvested them.


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Thesis Completion





Weeks, Arthur


Bachelor of Science (B.S.)


College of Engineering

Degree Program

Electrical and Computer Engineering


Dissertations, Academic -- Engineering;Engineering -- Dissertations, Academic







Access Status

Open Access

Length of Campus-only Access


Document Type

Honors in the Major Thesis

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