Inpection principle
The laboratory rice quality analyzer developed and produced by our company can replace manual work and can work 7*24 hours. It can detect the quality of rice with high precision. Detect and analyze the grains, worm-eaten grains, Gibberellin grains, damaged grains, black germs, impurities, etc., and form statistical reports from time to time to improve product safety and traceability. The parts in contact between the equipment and the sample are made of medical-grade materials, which are safe and hygienic, with intelligent design, simple operation and convenient maintenance.
Key technology
1. Automatic binarization: Use deep neural network to segment the foreground and background of the image. Compared with the traditional binarization method, it can be applied to a variety of lighting conditions, and the edge segmentation of rice is smoother, fast and robust High advantages.
2. Adhesive rice segmentation algorithm: The method based on connected domains cannot segment the adhered rice. The deep neural network is used to segment the adhered rice at an instance level, which can reach a speed of 1000fps and can process the adhered rice in real time.
3. Rice attribute recognition algorithm: adopts a lightweight neural network and integrates a semi-supervised learning method. The model can be iteratively optimized only by marking a small amount of data. It has the advantages of high accuracy, fast speed, and convenient deployment.
Inspection details
The analyzer combines traditional machine vision methods and artificial intelligence algorithms to analyze rice. First, traditional visual methods are used to segment the rice grains in the video frame, and then artificial intelligence algorithms are used to identify the attributes of the segmented rice grains and judge Whether there are insect-eaten, sprouting, mildew and other problems. At the same time, two high-resolution cameras were used to photograph the front and back of the rice, and the properties of the two sides were analyzed. Through the registration algorithm, the front and back of the rice are registered one by one, and their respective attributes are combined to obtain the attributes of a complete rice grain.
| Model.No | KVS-GR | Inspect speed | 900-1200/min |
| Size | 800*600*600mm | Weight | 110kg |
| Voltage | 220V±10%,50Hz | Current | 500-1000W |
| Ambient temperature | 10~30℃ | Environment humidity | Relative temperature≤85%
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After-sale service
The company has a complete technical service team and rapid response mechanism, and has dedicated service specialists for each customer, who can receive technical consultation and fault reports from customers at any time. And to ensure rapid response to customer emergencies, to ensure that customers receive satisfactory service.
During the epidemic or due to special reasons, when after-sales engineers are unable to reach the site, the service center can remotely adjust customer equipment for troubleshooting and technical consultation.
Product Tags:
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Insect-eaten AI Rice Grain Quality Analyzer Defect Inspection System Images
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