您好，欢迎访问浙江省农业科学院 机构知识库！
浙江省农业科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Estimating Maize-Leaf Coverage in Field Conditions by Applying a Machine Learning Algorithm to UAV Remote Sensing Images

文献类型： 外文期刊

作者： Zhou, Chengquan 1 ; Ye, Hongbao 1 ; Xu, Zhifu 1 ; Hu, Jun 1 ; Shi, Xiaoyan 1 ; Hua, Shan 1 ; Yue, Jibo 2 ; Yang, Guijun 2 ;

作者机构： 1.ZAAS, Inst Agr Equipment, Hangzhou 310000, Zhejiang, Peoples R China

2.Beijing Res Ctr Informat Technol Agr, Minist Agr PR China, Key Lab Quantitat Remote Sensing Agr, Beijing 100089, Peoples R China

3.Minist Agr, Key Lab Agriinformat, Beijing 100089, Peoples R China

关键词： machine learning; maize-leaf coverage; image segmentation; UAV remoting images

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.679； 五年影响因子：2.736 ）

ISSN：

年卷期： 2019 年 9 卷 11 期

页码：

收录情况： SCI

摘要： Leaf coverage is an indicator of plant growth rate and predicted yield, and thus it is crucial to plant-breeding research. Robust image segmentation of leaf coverage from remote-sensing images acquired by unmanned aerial vehicles (UAVs) in varying environments can be directly used for large-scale coverage estimation, and is a key component of high-throughput field phenotyping. We thus propose an image-segmentation method based on machine learning to extract relatively accurate coverage information from the orthophoto generated after preprocessing. The image analysis pipeline, including dataset augmenting, removing background, classifier training and noise reduction, generates a set of binary masks to obtain leaf coverage from the image. We compare the proposed method with three conventional methods (Hue-Saturation-Value, edge-detection-based algorithm, random forest) and a frontier deep-learning method called DeepLabv3+. The proposed method improves indicators such as Q(seg), S-r, E-s and mIOU by 15% to 30%. The experimental results show that this approach is less limited by radiation conditions, and that the protocol can easily be implemented for extensive sampling at low cost. As a result, with the proposed method, we recommend using red-green-blue (RGB)-based technology in addition to conventional equipment for acquiring the leaf coverage of agricultural crops.

  • 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：浙江省农业科学院

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