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Journal of Zhejiang University (Agriculture and Life Sciences)  2023, Vol. 49 Issue (6): 893-902    DOI: 10.3785/j.issn.1008-9209.2022.09.051
Agricultural engineering     
Research on semantic segmentation of parents in hybrid rice breeding based on improved DeepLabV3+ network model
Jia WEN1(),Xifeng LIANG1(),Yongwei WANG2
1.College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, Zhejiang, China
2.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, Zhejiang, China
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In order to solve the precision and real-time problems of parental discrimination in the processes of hybrid rice breeding and pollination, an improved DeepLabV3+ hybrid rice breeding parental discrimination semantic segmentation model based on a fully convolution neural network was proposed. The lightweight MobileNetV2 structure of the backbone network was used to replace the Xception structure of the original DeepLabV3+ backbone network, which is more suitable for the application on mobile devices. An extraction method of low-level features with close connection was proposed. The lower-level information and higher-level information were preliminarily concated as the input of the original lower-level information, which enabled the network to obtain more intensive information, thus enhancing the ability of the network to extract details. The results showed that the improved DeepLabV3+ network model had higher segmentation precision for parents of hybrid rice seed production than the original DeepLabV3+ network model, and reduced the model training time and image predictive time. Compared with other mainstream network models and advanced network models, it is found that the accuracy of different parameters of improved DeepLabV3+ network model is improved. This study provides a reference for the development of deep learning in the field of agricultural visual robots.

Key wordssemantic segmentation      deep learning      DeepLabV3+ network model      hybrid rice      lightweight model     
Received: 05 September 2022      Published: 25 December 2023
CLC:  TP391.4  
Corresponding Authors: Xifeng LIANG     E-mail:;
Cite this article:

Jia WEN,Xifeng LIANG,Yongwei WANG. Research on semantic segmentation of parents in hybrid rice breeding based on improved DeepLabV3+ network model. Journal of Zhejiang University (Agriculture and Life Sciences), 2023, 49(6): 893-902.

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关键词: 语义分割,  深度学习,  DeepLabV3+网络模型,  杂交水稻,  轻量化模型 
Fig. 1 Images of rice sample treatments on sunny and cloudy daysA. Sample 1 (cloudy day); B. Sample 1 flipped vertically (cloudy day); C. Sample 1 flipped horizontally (cloudy day); D. Sample 2 (sunny day); E. Sample 2 flipped vertically (sunny day); F. Sample 2 flipped horizontally (sunny day); G. Sample 3 (cloudy day); H. Sample 3 flipped vertically (cloudy day); I. Sample 3 flipped horizontally (cloudy day).
Fig. 2 Image labeling via Labelme software
Fig. 3 Structure chart of improved DeepLabV3+ network model
Fig. 4 Inverted residual structure of MobileNetV2 network module











Table 1 Overall operation steps of MobileNetV2 network module


Train parameter









Initial learning rate

0.000 10.000 1


Train image size



Train batch size



Number of train epochs



Number of train sets

1 3141 314


Number of validation sets



Number of test sets

Table 2 Train parameter setting of DeepLabV3+ network model
Fig. 5 Loss curve of improved DeepLabV3+ network model
Fig. 6 mIoU curve of improved DeepLabV3+ network model
Fig. 7 Comparison of loss between the improved DeepLabV3+ network model (A) and the original DeepLabV3+ network model (B)
Fig. 8 Comparison of mIoU between the improved DeepLabV3+ network model and the original DeepLabV3+ network model
Fig. 9 Comparison of multiple parameters between the improved DeepLabV3+ network model and the original DeepLabV3+ network model
Fig. 10 Comparison of segmentation effect images between the improved DeepLabV3+ network model and the original DeepLabV3+ network model





Original DeepLabV3+


Improved DeepLabV3+



Average precision/%



Average recall/%



Image predictive time/ms



Model training time

3 h 31 min 25 s2 h 48 min 14 s5 h 17 min 18 s11 h 37 min 45 s5 h 42 min 38 s
Table 3 Comparison results of parameters of different network models
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