Abstract:
In order to realize the rapid, comprehensive, and nondestructive identification of imperfect grains of
wheat, image processing, and CNN recognition methods can be applied by collecting the imperfect
grains and perfect grains mentioned in the national quality standard of wheat. Image processing can
make the output image have a better effect, which is convenient for image analysis and recognition.
Image preprocessing includes image acquisition, graying, median filtering, image segmentation, and
so on. The classical classification network LeNet-5 in convolutional neural network (CNN) takes the
preprocessed image as the input and adds batch normalization (BN). The BN algorithm can speed up the
decline speed of the training gradient, increase the convergence speed of the model, and increase the
stability of the model. It can recognize the image and evaluate the performance with the accuracy of
the test set. It avoids complex feature extraction steps and effectively improves the recognition rate of
wheat grains, which is of great significance to the intelligent detection and recognition of wheat. |
