您好，欢迎访问安徽省农业科学院 机构知识库！

Automatic extraction of wheat lodging area based on transfer learning method and deeplabv3+network

文献类型： 外文期刊

作者： Zhang, Dongyan 1 ; Ding, Yang 1 ; Chen, Pengfei 2 ; Zhang, Xiangqian 4 ; Pan, Zhenggao 5 ; Liang, Dong 1 ;

作者机构： 1.Anhui Univ, Natl Engn Res Ctr Agroecol Big Data Anal & Applic, Hefei 230601, Peoples R China

2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China

3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China

4.Anhui Acad Agr Sci, Inst Crops, Hefei 230031, Peoples R China

5.Suzhou Univ, Sch Informat & Engn, Suzhou 234000, Peoples R China

关键词： Wheat lodging; Multiple growth stages; Deep learning; Transfer learning; DeepLabv3+network

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2020 年 179 卷

页码：

收录情况： SCI

摘要： To provide technical support for lodging-resistant wheat breeding, post-disaster assessment, and analysis of factors affecting lodging, the dynamic and accurate extraction of lodging area is particularly important. Most existing methods of monitoring wheat lodging aim to extract the lodging area at a single growth stage, rendering the dynamic monitoring of wheat lodging highly difficult. Thus, this study was aimed at developing a method of estimating wheat lodging at multiple growth stages. For this purpose, nitrogen fertilizers were utilized at different levels to induce different lodging conditions in wheat fields. Unmanned aerial vehicles (UAVs) were used to obtain Red, Green and Blue (RGB) and multispectral images of the field at different wheat-growth stages. Based on these two types of images, a new method combining transfer learning and the DeepLabv3+ network is proposed herein to extract lodging areas at various wheat-growth stages. The proposed method was compared with the commonly used UNet for the extraction of the lodging area. The results show that the proposed method and UNet achieved dice coefficients of 0.82 and 0.75 (early flowering), 0.88 and 0.80 (late flowering), 0.89 and 0.86 (filling stage), 0.90 and 0.87 (early maturity), and 0.90 and 0.88 (late maturity), respectively, using RGB images; further, the proposed method and UNet achieved dice coefficients of 0.91 and 0.51 (early flowering), 0.89 and 0.28 (late flowering), 0.91 and 0.82 (filling stage), 0.93 and 0.76 (early maturity), and 0.92 and 0.56 (late maturity), respectively, at different wheat-growth stages using multispectral image data. Thus, the proposed method can be used to predict lodging at multiple wheat-growth stages, and it outperforms UNet. An effective tool for dynamic monitoring of wheat lodging has been proposed herein.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：安徽省农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台