OLISCAN
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Date
2026
Authors
Abeer Rawajbeh
Mais Dwaikat
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Abstract
The olive industry is one of the most vital agricultural sectors, and maintaining the quality of
olives before processing is essential to ensuring the production of high-quality olive oil. Tra-
ditional sorting methods rely mostly on manual labor, are time-consuming and prone to human
error, often resulting in quality disparities. Therefore, this project aims to design and imple-
ment an automated olive sorting system that improves the accuracy, speed and efficiency of the
classification process.
The importance of this project lies in its contribution to raising the quality of olive products,
reducing human effort, and reducing loss. The system will sort olives in two main stages: first
by size, then by color, with the ability to automatically identify and remove damaged olives and
leaves.
The main objectives of the project are to develop an intelligent, reliable, and affordable
machine capable of accurately classifying olives, improving production efficiency, and ensuring
the maintenance of consistent quality standards.
The working methodology is based on building a physical system (hardware) integrated with
sensors and camera modules. Sensors measure the size of each olive, while image processing
techniques are used through the camera to detect color differences and identify damaged or
unwanted materials. The system control logic will be implemented using a microcontroller to
automate the sorting and removal process.
Although similar sorting systems exist for fruits and vegetables, the number of specialized
olive sorting systems is small, especially in local markets. Hence, this project aims to bridge
this gap by providing an automated and economical olive sorting solution that meets regional
production needs.