Al Deep Learning-Based Sorting Machine      

Features :

 1. Ultra-high precision sorting
Multi-dimensional feature recognition: AI algorithms can analyze multi-dimensional features such as color, texture, shape, and surface defects (such as cracks and mildew) through deep learning, and solve the problem of missed detection caused by traditional color sorters relying on a single color threshold (such as transparent foreign bodies or impurities of similar colors).

Complex scene adaptation: Convolutional neural network (CNN) is used to deal with complex background noise, such as accurately identifying mixed tea stems and normal leaves in tea sorting, and the false positive rate can be reduced to less than 0.01%.

2. Dynamic adaptive optimization
E-learning capability: Using transfer learning technology, the device can quickly fine-tune the model after the new material goes live (e.g., the training time is reduced by 70% when migrating from rice sorting to coffee bean sorting).

Environmental self-calibration: The optical correction algorithm is integrated to compensate for light fluctuations or dust interference in real time, ensuring the stability of sorting in the continuous operation of the production line, and avoiding batch quality fluctuations caused by environmental changes of traditional equipment.

3. Revolution in efficiency and cost
Faster processing speed: The GPU-accelerated AI inference engine supports image processing of more than 1,000 frames per second, and with the high-speed valve array, the processing capacity of a single machine can reach 20 tons/hour (40% higher than that of traditional models).

Energy consumption optimization: Through reinforcement learning to optimize the trigger strategy of the spray valve, the compressed air consumption is reduced by 30%, and the annual energy saving cost exceeds 150,000 yuan (taking the 24-hour production line as an example).

                                                   Accepted                                    Rejected