Improved sap flow prediction: A comparative deep learning study based on LSTM, BiLSTM, LRCN, and GRU with stem diameter data
Introduction Recording sap flow in plants is essential to understanding water usage, especially for herbaceous species like tomatoes. While plant physiology research has progressed, there remains a gap in applying sap flow sensor data to these species. In this study, the predictive capabilities of R...
Saved in:
Main Authors: | Amora Amir, Marya Butt |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-12-01
|
Series: | Smart Agricultural Technology |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525003387 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Technical potential of the SAP HANA platform
by: M. A. Kovalyov
Published: (2023-08-01) -
Surface water quality prediction based on BOA-BiLSTM model(基于BOA-BiLSTM模型的地表水水质预测)
by: 章佩丽(ZHANG Peili), et al.
Published: (2025-05-01) -
A comparative study of multivariate CNN, BiLSTM and hybrid CNN–BiLSTM models for forecasting foreign exchange rate using deep learning
by: Elysee Nsengiyumva, et al.
Published: (2025-12-01) -
Xylem structure and the ascent of sap
by: Zimmermann, Martin Huldrych, 1926-
Published: (1983) -
Advanced Multi-Scale CNN-BiLSTM for Robust Photovoltaic Fault Detection
by: Xiaojuan Zhang, et al.
Published: (2025-07-01)