AOI Rice Grain Quality Analyzer Lab Equipment with Remote After-sale Service

Inspection background

The high-precision rice AI quick analyzer developed and produced by our company can be used in rice processing plants, rice storage, laboratories, and quality inspection centers. It can detect and analyze the sprouting grains, heterogeneous sprouting grains, grass seeds, chalky grains, worm-eaten grains, gibberellin grains, broken grains, black germs, impurities, etc., and generate statistical reports from time to time to improve product safety Performance and traceability, and at the same time can guide the improvement of rice quality.

Combine traditional machine vision methods and artificial intelligence algorithms to analyze rice. First, use traditional vision methods to segment the rice grains in the video frame, and then use artificial intelligence algorithms to identify the attributes of the segmented rice grains to determine whether there are insects. Moth, 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.

Equipment advantages:

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.

Features of system

• 1. AI algorithm: high stability, adapting to the environment and background disturbance; different defect samples can be automatically identified after training
• Dataization: Independent database, save multiple samples, analyze non-good products, and retain history
• Multi-orientation: 360 ° comprehensive inside and outside the samples
• High precision: detection accuracy can be high
• Modularization, can flexibly increase or decrease the detection function according to customer actual needs
• Easy to operate: It is easy to operate and easy to maintain
• Safety: Medical grade material manufacturing, fully compliant with medical supplies production environment

Our advantages