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GithubTraditional fruit and vegetable sorting relies on manual assessment of size and appearance, which is not only inefficient and prone to errors but also struggles to meet the demands of large-scale processing.
To achieve automated grading and quality inspection of fruits and vegetables, fruit and vegetable processing equipment manufacturers utilise SingTown Technology's OpenMV intelligent camera AI image recognition technology. Through AI machine vision and machine vision algorithms, real-time identification and grading of fruit volume and surface quality are achieved, enhancing automated sorting accuracy and production efficiency.

The fruit and vegetable sorting system, based on SingTown Technology's OpenMV intelligent camera AI image recognition technology, has developed an embedded AI vision solution that integrates volume detection and quality identification.
The system installs an OpenMV intelligent camera above the conveyor belt to capture real-time images of fruits. Through edge-deployed computer vision algorithms, it analyses the size, shape, and colour characteristics of the fruits, automatically classifying them into large, medium, and small grades. Based on the results, it controls the sorting mechanism to convey fruits of different specifications to their corresponding collection bins.
Simultaneously, on-device AI can detect surface imperfections, blemishes, and signs of decay on fruit, enabling the automatic distinction between quality produce and substandard items.
SingTown OpenMV smart camera features edge computing capability, enabling local execution of recognition models without external computational resources. Recognition latency is under 30 milliseconds, making it suitable for high-speed sorting scenarios.
Through SingTown Technology's OpenMV intelligent camera AI image recognition technology, the fruit and vegetable sorting system has achieved an upgrade from manual screening to AI visual quality inspection and automatic sorting, significantly enhancing sorting efficiency and quality control stability.

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