Research on semantic segmentation of parents in hybrid rice breeding based on improved DeepLabV3+ network model

doi:10.3785/j.issn.1008-9209.2022.09.051

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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Abstract

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 words： semantic 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: wenjia.cjlu@qq.com;lxfcjlu@163.com

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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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https://www.zjujournals.com/agr/10.3785/j.issn.1008-9209.2022.09.051 OR https://www.zjujournals.com/agr/Y2023/V49/I6/893

基于改进的DeepLabV3+网络模型的杂交水稻育种父母本语义分割研究

为解决杂交水稻育种授粉过程中父母本区分的精确性和实时性问题，本研究提出一种基于全卷积神经网络的、改进的DeepLabV3+杂交水稻育种父母本区分的语义分割模型。采用轻量化的主干网络MobileNetV2结构替换原DeepLabV3+的主干网络Xception结构，使之更适用于移动设备，并提出一种联系较为紧密的低层特征信息提取方法，将较低层次信息和较高层次信息初步融合作为原低层次信息的输入，使网络获得更加密集的信息，从而增强网络对于细节的提取能力。结果表明，改进的DeepLabV3+网络模型较原DeepLabV3+网络模型具有更高的杂交水稻制种父母本分割精度，并能够减少模型训练和图片预测时间。将改进后的DeepLabV3+网络模型与其他主流网络和先进网络模型对比发现，各项参数精度均有所提高。本研究为深度学习在农业视觉机器人领域中的发展提供了参考。

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

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

Table 3 Comparison results of parameters of different network models


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