<![CDATA[Avocado is an important commodity that requires proper postharvest handling, particularly insorting based on ripeness levels and fruit condition. Conventional methods that still rely onmanual inspection have limitations in terms of accuracy and efficiency. This study proposes areal-time sorting system based on YOLOv8n running on a Raspberry Pi 4 Model B with aGoogle Coral accelerator to detect avocados and classify their ripeness levels as well as damage.The model was trained with a custom dataset and achieved a precision of 77.2%, recall of84.4%, and a macro-average F1-Score of 80.6%. The system was integrated with a camera forimage acquisition and a mini conveyor for the sorting process. Experimental resultsdemonstrated reliable detection with an inference speed of 11.7 ms per image, and field testingsuccessfully classified avocados according to their categories. This system has proven to beeffective, cost-efficient, and supports the improvement of avocado postharvest quality.]]>
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