AI-Driven Fruit Quality Grading and Automated Conveyor Based Sorting System with Real Time Monitoring
DOI:
https://doi.org/10.15662/IJEETR.2026.0802114Keywords:
Automated Fruit Sorting, Computer Vision, Convolutional Neural Networks (CNN), ), Conveyor Belt System, Fruit Grading.Abstract
Fruits are still sorted and graded manually at retail markets and in the fruit processing industry, which leads to poor quality, high labor costs, and post-harvest losses. Accurate classification using traditional methods is challenging because of variations in color, texture, maturity, and surface defects. This study presents the design and development of an AI-driven automated fruit sorting system utilizing computer vision and Convolutional Neural Networks (CNN) for real-time categorization and quality evaluation. Fruits placed on a conveyor belt can be captured by the system using USB cameras. To categorize various fruit varieties and identify rotting fruits, image processing methods and a trained CNN model extract visual characteristics, such as color, shape, texture, and surface defects. Quality grades were assigned based on the classification results. Fruits are automatically sent to the appropriate bins when the sorting process is activated by the Arduino control unit. Additionally, the system offers a fruit-count display and real-time monitoring. The proposed approach provides a low-cost and scalable method for intelligent agricultural and food processing applications, while decreasing manual intervention, increasing processing speed, and improving grading accuracy.
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