A group of students at the College of Engineering, Sultan Qaboos University, have succeeded in developing a technological innovation that utilizes machine vision, which is one of the methods of artificial intelligence. They integrated it with robotics systems in order to automatically diagnose the quality of dates. This innovation has contributed to the integration of artificial intelligence applications in the food industry.
The project aims to improve the quality of food by leveraging artificial intelligence to automate the processes of detecting and classifying food, with the goal of enhancing production efficiency in factories and reducing the time required for inventorying and sorting dates, in addition to actively contributing to enhancing food security.
Asaad bin Said Al-Hina’i, a member of the student project team, stated that date manufacturing is considered one of the key sectors in the Sultanate of Oman. However, date producers face difficulties in the sorting process, especially when it is necessary to exclude rotten dates or those unsuitable for human consumption. Artificial intelligence technology plays a crucial role in developing date sorting methods within factories, and our project was created to improve the date sorting process by relying on computer vision algorithms and using robots.
He mentioned that the system enables the use of artificial intelligence in automatically classifying dates, automating the process of separating good dates from damaged ones, automatically distinguishing the quality levels of dates, detecting the condition of dates passing through the production line with a touch of artificial intelligence, then transferring them to the optimal path. It also calculates the quantity of sorted dates and determines the percentage of damaged dates among the usable ones. Ahmed bin Mohammed Al-Habsi, another member of the student project team, emphasized that integrating artificial intelligence is essential in classifying food items. It starts with introducing the dates to the production line using a conveyor belt, then capturing images of them above the production line with a high-definition camera. Through these images, healthy dates are distinguished from rotten ones using computer vision technology, and then classified through dedicated pathways in the production line. Saleh bin Yahya Al-Ghanami, another member of the student project team, noted that there are many challenges facing factories, such as manually classifying defective or unusable products, which can be costly economically and relying on human labor can lead to reducing date quality.
