Enhancing Corn Quality Control with the VideometerLab Autofeeder
In the world of agricultural technology, precision and efficiency are paramount. The VideometerLab with Autofeeder is a game-changer in the field of corn quality control, offering capabilities for high throughput multispectral analysis.
How does the VideometerLab with Autofeeder work?
The VideometerLab, when paired with the Autofeeder option, transforms into a powerful system capable of analyzing high throughput of samples. It is especially efficient for the analysis of different species of commodity grains, such as corn kernels, other cereals and oil crops.
One of the standout features of the VideometerLab Autofeeder is its ability to automate quality inspection. Utilizing advanced mathematical models, the system can analyze and classify products based on their quality traits. This automation not only speeds up the process but also ensures accuracy and consistency in the results. The system is designed to handle large samples quickly, making it an invaluable tool for improving efficiency in quality control processes.
The Autofeeder uses a vibrator to evenly distribute the grains from a funnel onto a belt. The belt then transports the granules under the VideometerLab scanner, where images are acquired, segmented, and analyzed. At the end of the measurement, a comprehensive summary report is automatically generated, providing detailed insights into the quality of the samples.
With its intuitive interface, the VideometerLab with Autofeeder, allows operators to use the instrument with minimal training, making it accessible to a wide range of users. The system’s ability to store analysis results in various formats, both visual and numerical, ensures that documentation is thorough and easily accessible for further processing.
Automated Corn Quality Inspection
The VideometerLab with Autofeeder runs with advanced machine learning models, which classify the grains based on their primary (color, shape, contour, size, etc.) and secondary characteristics (physical purity, damage, health, sprouting etc.). In the case of corn, an open classification model is available for VideometerLab with Autofeeder users.
As shown in the video above, this model drives the quality inspection of each, and every maize kernel based on its quality properties, generating both a numerical and visual report of the quality assessment performed. Classes include:
• Healthy maize kernels
• Broken maize kernels
• Mold damaged maize kernels
• Fermented maize kernels
• Insect damaged maize kernels
• Admixture and glumes
• Breeding seeds
By screening samples with this model, operators are able to efficiently get an overview of the quality of their corn products, before they are further processed in the value chain. Furthermore, it is possible to customize the classification model based on user and product needs, by, for example, adding extra feature classes, or weight densities in the measurement.
Conclusion
In conclusion, the VideometerLab with Autofeeder is a revolutionary tool that enhances the capacity for corn quality control. Its combination of automation, speed, and ease of use makes it an essential asset for any organization looking to improve their quality inspection processes. With minor adjustments the same system can be used for other cereals, and oil crops.