The image segmentation for maize ears is a key step in the process of automatic measurement of maize phenotypic parameters. The accuracy of image segmentation has a direct impact on the precision of subsequent calculation on phenotype parameters of maize ear. A segmentation model of maize ear image was proposed to solve the problem of the unclear boundary between adjacent regions in maize ear images. The model realized the standard U-Net model based on the encoder-decoder principle and could integrate feature selection, feature extraction and feature classification. We build two different models with fixed learning rate and stepwise decay learning rate separately. The experimental results demonstrate that using the stepwise decay learning rate in the training process can not only obtain a faster convergence speed but also ensure better convergence value. Overfitting is a common problem in the deep learning model. We adopt regularization and dropout strategies to suppress overfitting. The experimental results show that the regularization strategy can successfully suppress the overfitting problem in this model. The experiment results showed that the accuracy of the model on training set was 93.91% and on test set was 93.45%. The accuracies on each data set were very close, which showed that the model had effective generalization performance.
Segmentation of Maize Ear Bold Tip Based on U-Net
Lect. Notes Electrical Eng.
International Conference on Electrical and Information Technologies for Rail Transportation ; 2021 October 21, 2021 - October 23, 2021
Proceedings of the 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2021 ; Kapitel : 68 ; 609-616
2022-02-23
8 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Segmentation of Maize Ear Bold Tip Based on U-Net
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