SingTown Technology's AI vision automatically identifies nuts, allowing roasting machines to precisely control the heat.

In intelligent roasting equipment, different materials (such as peanuts, sunflower seeds, chestnuts, etc.) require matching with distinct heating curves and stirring strategies. Manual setup 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 intelligent camera to build a machine vision-based material recognition system.

The system captures images of raw materials inside the hopper via a camera, automatically identifies the currently loaded food type, and then calls the corresponding frying program. The primary recognition targets are nut-based foods with particle sizes of 1–5 cm. Installed above the equipment, it can capture the hopper contents within an adjustable distance. The system must distinguish between different varieties such as peanuts, melon 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 intelligence level and user experience.

The manufacturer of stir-frying robots, based on SingTown Technology's OpenMV intelligent camera material recognition system, integrates AI image recognition, color 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 analyzes and classifies the color, texture, and shape characteristics of raw materials such as peanuts, melon seeds, and chestnuts through color space conversion (RGB-HSV) and morphological feature extraction algorithms. By training sample models, the system can quickly determine the type of material in the current bin, output corresponding identification signals, and automatically match the robot's frying program.

During the detection process, the OpenMV embedded vision module can automatically adjust exposure and white balance according to different lighting conditions to maintain stable and accurate recognition. Even if there are deviations or stacking of material positions in the hopper, the system can accurately identify them through region segmentation and multi-feature fusion algorithms (colour + texture + contour shape). The recognition results are sent to the main control microcontroller via serial port or bus to achieve automatic parameter switching.

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

SingTown Technology's OpenMV intelligent camera features low cost, easy deployment, and strong anti-interference capability. It is suitable for edge deployment in various equipment such as roasting machines, nut packaging machines, and food sorting systems, assisting the food automation processing industry in upgrading towards intelligence.