您好，欢迎访问中国农业科学院 机构知识库！

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

ST-YOLO: a deep learning based intelligent identification model for salt tolerance of wild rice seedlings

文献类型： 外文期刊

作者： Yao, Qiong 1 ; Pan, Pan 1 ; Zheng, Xiaoming 3 ; Zhou, Guomin 5 ; Zhang, Jianhua 1 ;

作者机构： 1.Chinese Acad Agr Sci, Agr Informat Inst, Beijing, Peoples R China

2.Henan Univ, Coll Agr, Kaifeng, Peoples R China

3.Chinese Acad Agr Sci, Natl Nanfan Res Inst, Sanya, Peoples R China

4.Chinese Acad Agr Sci, Inst Crop Sci, Beijing, Peoples R China

5.Minist Agr & Rural Affairs, Nanjing Inst Agr Mechanizat, Nanjing, Peoples R China

6.Natl Agr Sci Data Ctr, Beijing, Peoples R China

7.Chinese Acad Agr Sci, Inst Western Agr, Changji, Xinjiang, Peoples R China

关键词： wild rice; salt resistance grade; intelligent evaluation identification; deep learning; seedling

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.8； 五年影响因子：5.7 ）

ISSN： 1664-462X

年卷期： 2025 年 16 卷

页码：

收录情况： SCI

摘要： Background In response to the limited models for salt tolerance detection in wild rice, the subtle leaf features, and the difficulty in capturing salt stress characteristics, resulting in low recognition and detection rates and accuracy, a deep learning-based ST-YOLO wild rice seedling salt tolerance phenotype evaluation and identification model is proposed.Method In order to improve accuracy and achieve model lightweighting, a multi branch structure DBB (Diverse Branch Block) is used to replace the convolutional layers in the C2f module, and a reparameterization module C2f DBB is proposed to replace some C2f modules. Diversified feature extraction paths are introduced to enhance the ability of feature extraction; Introducing CAFM (Context Aware Feature Modulation) convolution and attention fusion modules into the backbone network to enhance feature representation capabilities while improving the fusion of features at various scales; Design a more flexible and effective spatial pyramid pooling layer using deformable convolution and spatial information enhancement modules to improve the model's ability to represent target features and detection accuracy.Results The experimental results show that the improved algorithm improves the average precision by 2.7% compared with the original network; the accuracy rate improves by 3.5%; and the recall rate improves by 4.9%.Conclusion The experimental results show that the improved model significantly improves in precision compared with the current mainstream model, and the model evaluates the salt tolerance level of wild rice varieties, and screens out a total of 2 varieties that are extremely salt tolerant and 7 varieties that are salt tolerant, which meets the real-time requirements, and has a certain reference value for the practical application.

  • 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：中国农业科学院

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