检索结果-内蒙古大学图书馆

U + LSTM-F: A data-driven growth process model of rice seedlings

Ecological Informatics 2024年 84卷

作者： Tian, Xin Cao, Weifan Liu, Shaowen Zhang, Buyue Wei, Junshuo Ma, Zheng Gao, Rui Su, Zhongbin Dong, Shoutian Key Laboratory of Northeast Smart Agricultural Technology Ministry of Agriculture and Rural Affairs Heilongjiang Province Harbin 150030 China School of Electrical and Information Northeast Agricultural University Heilongjiang Province Harbin 150030 China Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen 518060 China School of Resource and Environment Northeast Agricultural University Heilongjiang Province Harbin 150030 China

Accurately predicting the growth status of rice seedlings and understanding their growth rate and health status in a timely manner helps adjust the growth cycle and management measures. By predicting the growth status of the seedlings, the best time for transplanting can be selected, improving the survival rate and overall health of the seedlings, thereby enhancing yield and quality. Therefore, this study proposes a data-driven time-series model, the U + LSTM-F model, for predicting the growth status of Wuyou Rice 4 seedlings. First, the U-Net model is employed to segment sequentially collected images, extracting features such as leaf age and stem length of the rice seedlings. Subsequently, the collected ambient temperature and humidity data are aligned with the leaf age and stem length data. Finally, the LSTM model is used for time-series analysis, enabling the model to learn the temporal relationship between environmental and growth data and predict the growth trend of the rice seedlings. Additionally, an attention mechanism is introduced to enhance model performance, and the model's effectiveness is evaluated using multiple quantitative metrics. The proposed model achieves an RMSE of 0.032 and MAPE of 0.895 % for leaf age prediction, and an RMSE of 0.067 and MAPE of 0.814 % for stem length prediction. The experimental results show that this data-driven approach, which combines growth data with environmental data, exhibits high accuracy in predicting the leaf age and stem length of rice seedlings. This provides a more accurate tool for predicting the growth of rice seedlings, offering valuable insights for rice seedling cultivation research. © 2024

关键词： Attention mechanism data-driven time-series model Features extraction Rice seedlings growth status

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：