您好，欢迎访问上海市农业科学院 机构知识库！
上海市农业科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Counting Dense Leaves under Natural Environments via an Improved Deep-Learning-Based Object Detection Algorithm

文献类型： 外文期刊

作者： Lu, Shenglian 1 ; Song, Zhen 1 ; Chen, Wenkang 1 ; Qian, Tingting 2 ; Zhang, Yingyu 2 ; Chen, Ming 1 ; Li, Guo 1 ;

作者机构： 1.Guangxi Normal Univ, Coll Comp Sci & Engn, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China

2.Shanghai Acad Agr Sci, Agr Informat Inst Sci & Technol, Shanghai 201403, Peoples R China

关键词： deep learning; plant phenotyping; leaf detection; object recognition

期刊名称：AGRICULTURE-BASEL （ 影响因子：2.925； 五年影响因子：3.044 ）

ISSN：

年卷期： 2021 年 11 卷 10 期

页码：

收录情况： SCI

摘要： The leaf is the organ that is crucial for photosynthesis and the production of nutrients in plants; as such, the number of leaves is one of the key indicators with which to describe the development and growth of a canopy. The irregular shape and distribution of the blades, as well as the effect of natural light, make the segmentation and detection process of the blades difficult. The inaccurate acquisition of plant phenotypic parameters may affect the subsequent judgment of crop growth status and crop yield. To address the challenge in counting dense and overlapped plant leaves under natural environments, we proposed an improved deep-learning-based object detection algorithm by merging a space-to-depth module, a Convolutional Block Attention Module (CBAM) and Atrous Spatial Pyramid Pooling (ASPP) into the network, and applying the smooth(L1) function to improve the loss function of object prediction. We evaluated our method on images of five different plant species collected under indoor and outdoor environments. The experimental results demonstrated that our algorithm which counts dense leaves improved average detection accuracy of 85% to 96%. Our algorithm also showed better performance in both detection accuracy and time consumption compared to other state-of-the-art object detection algorithms.

  • 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：上海市农业科学院

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