Using SingTown Technology's AI vision to automatically identify nuts, the roasting machine can also "grasp" the heat precisely.

In intelligent roasting equipment, different materials (such as peanuts, melon 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.

This system captures images of the raw materials inside the hopper through a camera, automatically identifies the type of food currently placed, and then calls the corresponding stir-frying program. The primary recognition targets are nut-based foods with a particle size of 1–5 centimeters. 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 stir-frying matching, thereby enhancing the equipment's level of intelligence and user experience.

The manufacturer of stir-frying robots utilizes SingTown Technology's OpenMV intelligent camera-based material recognition system, integrating 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, sunflower 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 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 work. 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 (color + texture + contour shape). The recognition results are sent to the main control microcontroller via serial port or bus to achieve automatic parameter switching.

Actual measurement data shows that 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 below 0.5 seconds.

SingTown 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, helping the food automation processing industry upgrade towards intelligence.