There are some technical and operational difficulties in the tobacco leaf sorting process of traditional optical sorting machine, which are mainly reflected in the following aspects:
- Tobacco leaves have different shapes and may be bent, folded or damaged, which makes it difficult for the color sorter to accurately judge their quality during identification, especially when the shape and color of low-quality tobacco leaves are relatively consistent.
- Tobacco leaves tend to stick to each other and cannot be thrown far. Hair, feathers, strings, insects, etc. are easily found in tobacco leaves.
Golden optical sorting machine for tobacco leaf
Sticky materials and light materials have always troubled the market. In order to solve the problems that are easy to stick and cannot be thrown far, we have developed and designed an ultra-clear intelligent crawler sorting machine, which can replace part of the manual work and remove foreign objects such as hair, feathers, string, and insect corpses. It has a high selection rate and low material loss, helping companies solve the problem of difficulty in recruiting workers and create high-quality products.
Equipped with an ultra-high-resolution camera, tiny foreign objects such as hair can be identified, and the 3-5CM between the mouthpiece and the front roller can be better removed.
Tobacco leaf color, shape, texture, humidity and other dimensions are multiple inputs that deep learning technology can effectively process. Deep learning algorithms, especially convolutional neural networks (CNNs), can analyze multiple levels of information simultaneously and identify complex feature combinations to make more accurate judgments.
