干旱区生态环境知识资源中心(Arid): An approach for multi-depth soil moisture prediction in alfalfa based on a dual-branch combined model

Arid

DOI	10.1016/j.compag.2024.109038
	An approach for multi-depth soil moisture prediction in alfalfa based on a dual-branch combined model
	Liu, Rui; Lu, Lifeng; Ge, Yongqi; Dong, Liguo; Zhou, Juan
通讯作者	Ge, YQ
来源期刊	COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN	0168-1699
EISSN	1872-7107
出版年	2024
卷号	222
英文摘要	Rainfall or irrigation induces substantial fluctuations in soil moisture at various depth. Improving the accuracy of multi-depth soil moisture prediction during these events is crucial for precision irrigation. This study proposes a dual-branch combined model for multi-depth soil moisture prediction in alfalfa (ALFSMP-DBCM). The model employs fully connected layers in the left branch to extract rainfall and irrigation features, while the right branch uses convolutional residual networks to model soil moisture relationships. The fusion of these branches enables effective multi-depth soil moisture prediction for alfalfa. Field experiments were designed and conducted in the Ningxia Irrigation Area of the Yellow River (NIR). A comprehensive dataset, comprising 19,763 data points on alfalfa growth environment in the different precipitation years (2017, 2018, and 2022), was established and utilized as model training data. Three classical deep learning models were employed for comparison. Results demonstrated that the ALFSMP-DBCM model effectively predicted multi-depth soil moisture during all alfalfa growth stages. The R2 of the model within the range of 0.911 to 0.992, with average MAE, MSE, and RMSE within the range of 0.29% to 0.58%, 0.22% to 0.56%, and 0.47% to 0.68%, respectively. Compared to the ANN, LSTM, and BiLSTM models, the ALFSMP-DBCM model improved the prediction accuracy of soil moisture at multi-depth by 7.19%, 11.90%, and 10.32%, respectively. The model exhibited robust performance under instantaneous water replenishment conditions and stability in predicting multi-depth soil moisture with different delay days. These findings provide a valuable reference for precision irrigation regulation and field management of alfalfa in arid and semi-arid regions.
英文关键词	Alfalfa Multi -depth soil moisture prediction Dual -branch combined model Deep learning
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:001244208500001
WOS关键词	NEURAL-NETWORKS ; WATER CONTENT ; PATTERNS
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS研究方向	Agriculture ; Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403255
推荐引用方式 GB/T 7714	Liu, Rui,Lu, Lifeng,Ge, Yongqi,et al. An approach for multi-depth soil moisture prediction in alfalfa based on a dual-branch combined model[J],2024,222.
APA	Liu, Rui,Lu, Lifeng,Ge, Yongqi,Dong, Liguo,&Zhou, Juan.(2024).An approach for multi-depth soil moisture prediction in alfalfa based on a dual-branch combined model.COMPUTERS AND ELECTRONICS IN AGRICULTURE,222.
MLA	Liu, Rui,et al."An approach for multi-depth soil moisture prediction in alfalfa based on a dual-branch combined model".COMPUTERS AND ELECTRONICS IN AGRICULTURE 222(2024).