SingTown's AI vision technology automatically identifies nuts, enabling roasting machines to precisely control the temperature.

In intelligent roasting equipment, different materials (such as peanuts, sunflower seeds, chestnuts, etc.) require matching with distinct heating curves and stirring strategies. Manual configuration is not only time-consuming but also prone to errors. To achieve automation and precise control of the roasting process, the roasting robot manufacturer plans to adopt SingTown Technology's OpenMV smart camera to build a machine vision-based material recognition system.

The system captures images of the raw materials inside the hopper via a camera, automatically identifies the type of food currently placed, and then calls the corresponding frying program. The primary recognition targets are nut-based foods with a particle size of 1–5 centimetres. The camera is installed above the equipment and can capture the contents of the hopper within an adjustable distance. The system must be able to distinguish between different varieties such as peanuts, sunflower seeds, and chestnuts, maintaining stable recognition even with minor positional deviations or angle variations. The goal is to enable the robot to automatically identify material types, achieve unmanned loading and intelligent frying matching, and enhance the equipment's level of intelligence and user experience.

The manufacturer of stir-frying robots utilises SingTown Technology's OpenMV intelligent camera-based material recognition system, which integrates AI image recognition, colour texture analysis, and machine learning classification algorithms to automatically identify material types in real-time.

The system captures images of the material bin using high-resolution cameras and employs colour space conversion (RGB-HSV) and morphological feature extraction algorithms to analyse and classify the colour, texture, and shape characteristics of raw materials such as peanuts, sunflower seeds, and chestnuts. By training sample models, the system can swiftly determine the type of material currently in the bin, output corresponding identification signals, and automatically match the robotic stir-frying programme.

During the detection process, the OpenMV embedded vision module automatically adjusts exposure and white balance according to varying lighting conditions to maintain stable and accurate recognition. Even if the material position in the hopper deviates or is stacked, the system can accurately identify it through region segmentation and multi-feature fusion algorithms (colour + texture + contour shape). The recognition results are transmitted to the main control microcontroller via serial port or bus, enabling automatic parameter switching.

Test data shows that under standard lighting conditions, the system achieves an identification accuracy rate of over 95% for three common roasted snack ingredients (peanuts, sunflower seeds, chestnuts), with a response time under 0.5 seconds.

SingTown's OpenMV smart camera offers low cost, easy deployment, and strong anti-interference capabilities. It is suitable for edge deployment in various equipment such as roasting machines, nut packaging machines, and food sorting systems, supporting the intelligent upgrade of the food automation processing industry.