Parameter
AZC Series
|
Product model |
Machine Weight |
Machine Output (kg) |
Machine Power |
Machine Output ratio
|
Color Selection Accuracy |
Minimum Resolution |
Dimension |
Machine Voltage |
|
AZC1 |
600 |
600 |
1.5 |
≥10：1 |
＞99.99% |
0.01 |
75×162×155 |
AC220V/50HZ |
AT-Su Series
|
Product model |
Machine Weight |
Machine Output (kg) |
Machine Power |
Machine Output ratio
|
Color Selection Accuracy |
Minimum Resolution |
Dimension |
Machine Voltage |
|
AT2Su |
600 |
1200 |
1.5 |
≥10：1 |
＞99.99% |
0.01 |
250×131×150 |
AC220V/50HZ |
|
AT4Su |
800 |
1400 |
1.5 |
≥10：1 |
＞99.99% |
0.01 |
270×140×165 |
AC220V/50HZ |
|
AT6Su |
1000 |
1600 |
2.0 |
≥10：1 |
＞99.99% |
0.01 |
290×131×164 |
AC220V/50HZ |
|
AT8Su |
1200 |
2000 |
2.5 |
≥10：1 |
＞99.99% |
0.01 |
300×150×170 |
AC220V/50HZ |
AZM Series
|
Product model |
Machine Weight |
Machine Output (kg) |
Machine Power |
Machine Output ratio
|
Color Selection Accuracy |
Minimum Resolution |
Dimension |
Machine Voltage |
|
AZM300M |
200 |
400 |
0.3 |
≥10：1 |
＞99.99% |
0.01 |
150×88×124 |
AC220V/50HZ |
Feature
|
● Classify and screen guavas based on their color, shape, flaws, and other characteristics. |
|
● By using high-speed cameras and AI algorithms, it is possible to quickly distinguish maturity, skin damage, or quality differences, thereby improving the commercialization rate of guavas. |
|
● When non-conforming products are detected, precise airflow is triggered to blow the guavas into different outlets for grading. |
|
● The guavas sorting standards such as size, sugar content, and defect threshold can be adjusted according to customer needs. |
The guava color sorter uses a high-resolution CCD camera or near-infrared sensor to collect the appearance and shape information of guava, and then uses deep learning algorithms to identify its maturity, defects, sugar content, etc. Finally, the unqualified guava is blown out through a high-quality solenoid valve, thus achieving automatic sorting of guava.
The guava color sorter can screen guava based on its maturity, skin integrity, size specifications, sugar content, internal quality, and other characteristics. During the sorting process, it is necessary to reduce collisions, prevent skin damage from shortening the storage period, and control the environment of the sorting workshop to prevent high temperatures from accelerating fruit softening and decay. For different varieties and qualities of guava, color selection parameters also need to be adjusted to avoid misjudgment. The guava color sorter can perform multi-spectral detection, and its adaptive light source can eliminate light interference, ensuring accurate color judgment. At the same time, its efficient sorting can be applied to large-scale processing and production, and customers can also adjust the configuration according to production needs. Using a color sorting machine to screen guava has the advantages of high efficiency, high precision, low labor cost, data-driven management, and applicability to various screening needs.
The guava color soter plays a key role in the commercialization of fresh fruits, screening of deep processed raw materials, and the demand for e-commerce branding, which can significantly enhance the product rate and market competitiveness of guava. In the future, color sorter will achieve more intelligent sugar analysis and internal quality testing. Against the backdrop of consumer upgrading and continuous growth in export demand, intelligent color sorter have become the core driving force for improving the quality of the guava industry, and their application prospects are very broad.
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