基于深度学习的全自动穴盘苗分级系统设计OA

Considering the low efficiency of current tray seedling grading operations and the low accuracy of image classification,this paper proposes a fully automatic tray seedling grading system based on deep learning algorithms.It uses Siemens PLC as the controller and a three-axis robotic arm as the sorting executive mechanism.The system employs data augmentation methods to expand the collected tray seedling detection dataset,increasing the amount of training samples.It introduces the deep learning ResNet50 algorithm model,trains the model to obtain the optimal detection parameters,and performs virtual simulation and prototype verification on the proposed fully automatic tray seedling grading system.The test results show that the tray seedling grading system can effectively complete the transportation,recognition,and sorting of seedlings.The model's detection precision is 98.72%,the recall rate is 96.36%,the inference time is within 35 ms,and the intelligent sorting success rate is above 95%,verifying the effectiveness and feasibility of the fully automatic tray seedling sorting system,with stronger generalization and robustness.