Product background
Compared with crops such as rice and wheat, corn has strong drought
tolerance, barren tolerance, and good environmental adaptability.
The nutritional value of corn is high. With the improvement of
people's living standards, the pursuit of quality in corn
consumption and processing has become higher. Visual inspection of
corn defects has become an important method to improve quality.
The quality of corn itself is an important guarantee for the value
of corn. The corn online quality detector produced by our company
adopts brand-new AI technology, which can detect the state of corn
with high efficiency and high precision. Disease spots,
insect-eaten grains, broken grains, soil, impurities (sands,
bricks, weed seeds, etc.) are tested, and statistical reports are
formed online.
Inspection principle
The solution realizes multi-corn seed image segmentation according
to the software functions of the vision system, performs
preprocessing operations, and drags the software functions such as
patch pickling, patch counting and other functions to realize corn
bug eye detection, corn blackhead detection, corn mildew detection,
and corn damage. Detection, etc., make the image information
enhancement detection effect more accurate, and distinguish the
qualified corn quality from the corn shape characteristics, color,
texture and other information.
Device parameters
| power | <200 W 220~230V |
| voltage range | Single-phase alternating current 200~240V,50HZ |
| Environment temperature | -10℃～+45℃(No icing) |
| Environment humidity | Below 85% (no condensation) |
Detection details
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 corn edge segmentation is smoother,
fast and robust High advantages.
2. Adhesive corn segmentation algorithm: The method based on
connected domains cannot segment the adhered corn. The deep neural
network is used to segment the adhered corn at an instance level,
which can reach a speed of 1000fps and can process the adhered corn
in real time.
3. Corn 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.
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.
After the equipment arrives at the customer site, the after-sales
engineer arrives in time to carry out equipment installation,
commissioning, and operation training. The product quality of the
whole machine is traceable, and the quality warranty period is 1
year from the date of acceptance. In the event of non-human faults
during the warranty period, after-sales engineers will quickly
arrive at the site or provide remote guidance for free maintenance.
