| 基于改进Unet的小麦茎秆截面参数检测 |
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| 引用本文： | 陈燕,朱成宇,胡小春,王令强.基于改进Unet的小麦茎秆截面参数检测[J].农业机械学报,2021,52(7):169-176. |
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| 作者姓名： | 陈燕 朱成宇 胡小春 王令强 |
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| 作者单位： | 广西大学;广西财经学院 |
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| 基金项目： | 国家自然科学基金项目（31771775）和广西自然科学基金项目(2020GXNSFAA159090) |
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| 摘 要： | 针对小麦茎秆截面显微图像分割过程的复杂性，融合ResNet50和Unet网络构建维管束和背景区域的语义分割模型Res-Unet，搭建对小麦茎秆截面、髓腔、厚壁和背景的语义分割模型Mobile-Unet，可实现对小麦茎秆截面尺寸、髓腔尺寸和维管束面积等微观结构参数的检测。针对小麦样本数据集，通过深度学习中迁移学习的共享参数方式，将训练好的ResNet50网络权重应用到茎秆截面切片图像的网络模型上。结果表明，与同类方法相比，相关参数在精度上均有较大提升，全部参数的识别率超过97%，最高可达99.91%，平均每幅图像检测只需21.6s，与已有图像处理方法（110s）相比，处理速度提升了80.36%。模型评估的准确率、召回率、F1值和平均交并比均达到90%。本文方法可用于小麦茎秆微观结构的高通量观察和参数测定，为作物抗倒伏研究奠定了技术基础。
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| 关 键 词： | 小麦茎秆 截面参数 显微结构 深度学习 图像处理 语义分割 |
| 收稿时间： | 2020/11/26 0:00:00 |
Detection of Wheat Stem Section Parameters Based on Improved Unet |
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| CHEN Yan,ZHU Chengyu,HU Xiaochun,WANG Lingqiang.Detection of Wheat Stem Section Parameters Based on Improved Unet[J].Transactions of the Chinese Society of Agricultural Machinery,2021,52(7):169-176. |
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| Authors: | CHEN Yan ZHU Chengyu HU Xiaochun WANG Lingqiang |
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| Institution: | Guangxi University;Guangxi University of Finance and Economics |
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| Abstract: | The microstructure is closely related to mechanical strength of the stem, which plays an important role in crop lodging resistance. However, the lack of effective methods in identification and estimation of the parameters severely restricted the related researches. In view of the complexity of wheat stalk cross-section microscopic image data set, ResNet50 and Unet deep learning network were used to build a semantic segmentation model Res-Unet for vascular bundles and background regions. MobileNet and Unet networks was combined to build a cross-section, marrow cavity and background. The semantic segmentation model Mobile-Unet measured the relevant parameters of lodging resistance such as the cross-sectional size of the wheat stem, the size of the pulp cavity and the area of the vascular bundle. For small sample data sets, the trained ResNet50 network weights were applied to the network model of wheat stalk cross-sectional slice images through the shared parameter method of transfer learning in deep learning. The results showed that compared with the previous studies, the key parameters greatly improved in accuracy, and the recognition rate of all parameters exceeded 97%, and the highest was 99.91%. Moreover, it only took 21.6s to detect a single image, which was an average increase of 80.36% over the 110s of existing image processing methods. In addition, the model evaluation accuracy rate, recall rate, F1 value and mean intersection over union (mIoU) index values all reached 90%. In conclusion, the method developed was accurate, real-time and effective, and can serve as one of important techniques for the further studies of crop lodging resistance. |
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| Keywords: | wheat stem section parameters microstructure deep learning image processing semantic segmentation |
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