ArticleSensors (Basel, Switzerland)2023
Development of a Soil Moisture Prediction Model Based on Recurrent Neural Network Long Short-Term Memory (RNN-LSTM) in Soybean Cultivation.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 49 citations in OpenAlex.
- A thermodynamics-integrated physics-guided neural network for soil temperature forecasting.Scientific reports · 2026Article
- Integrating Artificial Intelligence in Environmental Monitoring: A Paradigm Shift in Data-Driven Sustainability.EcoHealth · 2026Review
- A lightweight soil moisture prediction model based on irrigation cycle segmentation and Kalman filtering.Frontiers in plant science · 2026Article
- Development of long short-term memory models using rainfall and soil moisture to predict soil moisture dynamics.PloS one · 2026Article
- From Traditional Machine Learning to Fine-Tuning Large Language Models: A Review for Sensors-Based Soil Moisture Forecasting.Sensors (Basel, Switzerland) · 2025Review
- Integrating Artificial Intelligence and Biotechnology to Enhance Cold Stress Resilience in Legumes.Plants (Basel, Switzerland) · 2025Review
- Establishing a periodic SM model with Fourier analysis for enhancing global soil moisture forecasting.Scientific reports · 2025Article
- Energy efficient and robust node localization in WSNs using LSTM optimized DV hop framework to mitigate multihop localization errors.Scientific reports · 2025Article
- Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety.Foods (Basel, Switzerland) · 2025Review
- The optimization path of agricultural industry structure and intelligent transformation by deep learning.Scientific reports · 2024Article
- Optimized EWT-Seq2Seq-LSTM with Attention Mechanism to Insulators Fault Prediction.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Due to climate change, soil moisture may increase, and outflows could become more frequent, which will have a considerable impact on crop growth. Crops are affected by soil moisture; thus, soil moisture prediction is necessary for irrigating at an appropriate time according to weather changes. Therefore, the aim of this study is to develop a future soil moisture (SM) prediction model to determine whether to conduct irrigation according to changes in soil moisture due to weather conditions. Sensors were used to measure soil moisture and soil temperature at a depth of 10 cm, 20 cm, and 30 cm from the topsoil. The combination of optimal variables was investigated using soil moisture and soil temperature at depths between 10 cm and 30 cm and weather data as input variables. The recurrent neural network long short-term memory (RNN-LSTM) models for predicting SM was developed using time series data. The loss and the coefficient of determination (R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.