- All parts that come into contact with the fruit are thickened with soft bags, and the grading process does not damage the fruit. It can be sorted into 8 levels, with a computer system and a freely adjustable specification range.
- The equipment is sturdy and durable, and equipped with a feeding tray, which can add Al visual equipment to select rotten fruits, making it versatile for one machine.
- The number of levels can be adjusted at will, and the size of levels can be adjusted at will.
- Adopting all copper motors, both 220v and 380v can be used, and 19000 pieces can be sorted in one hour.
- The working track is made of food grade silica gel, which is elastic, harmless and anti-aging.
Suitable for sorting and processing of non- -standard fruits and vegetables such as sugar oranges (with branches and leaves), onions, tomatoes, potatoes, pears, dragon fruits, kiwifruit, etc. It can also be paired with automatic feeding and cleaning air drying for initial processing.The visual system adopts a combination of AI vision, spectral analysis, deep learning algorithms, and other technologies.Based on machine vision and image processing technology, an intelligent recognition and grading system for potato non-destructive testing is constructed from the aspects of size, potato shape, external defects, and internal defect weight indicators. After cleaning the external surface of potatoes, they are restored to their external features through
stereo vision technology, and defective potatoes are screened out. They are then transported to corresponding grading boxes according to their quality and size, achieving rapid, real-time, objective, and non-destructive detection, recognition, and grading of potatoes, making the entire process automated and inelligent, and promoting large-scale production. To achieve real-time detection, recognition, and
grading of potatoes, this product constructs an intelligent grading software and hardware device for potatoes. Based on Qt and VC+ +, software development and design are carried out to achieve itelligent grading of potatoes.By using methods such as deep learning and spectral analysis, not only can defective potato blocks such as insect infestation, damage, germination, and green skin (black heart disease) be selected, but also the size (starch content, sugar content) of potato can be quickly and accurately perceived without damage. The rapid and unmanned operation of the assembly line has been achieved, achieving the interdisciplinary and interdisciplinary integration of artificial itelligence and agriculture.