Ahamed, A J SBenorith, L2025-09-162025-09-162025-07-083093-5768https://rda.sliit.lk/handle/123456789/4187This paper presents the design and implementation of an automated apple sorting system that integrates machine vision techniques with embedded control for real-time classification and sorting of apples. The system employs a Raspberry Pi 4 as the primary processing unit, using a YOLOv11 model for fruit detection and classification, while an Arduino Nano manages weight measurement via a load cell. Real-time images of apples on a conveyor belt are captured, processed, and classified into four categories: Good Red, Good Green, Bad Red, and Bad Green. Sorting mechanisms, including servos and actuate based on classification results, with an integrated LCD and cloudbased Google Sheets providing monitoring and logging. The system demonstrates high classification accuracy and reliable sorting performance, offering a cost-effective solution for small to mid-scale agricultural applicationsenFruit GradingMachine VisionDeep LearningConvolutional Neural NetworksArticlehttps://doi.org/10.54389/CLAQ4405