ArticleSensors (Basel, Switzerland)2020
Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of IoT Solutions for Smart Farming.Sensors (Basel, Switzerland) · 2020Pooled it
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- From Lidar Measurement to Rotor Effective Wind Speed Prediction: Empirical Mode Decomposition and Gated Recurrent Unit Solution.Sensors (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Biological Sciences.Life (Basel, Switzerland) · 2022Review
- Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions.Sensors (Basel, Switzerland) · 2022Article
- Deep Prediction Model Based on Dual Decomposition with Entropy and Frequency Statistics for Nonstationary Time Series.Entropy (Basel, Switzerland) · 2022Article
- Research on Rice Yield Prediction Model Based on Deep Learning.Computational intelligence and neuroscience · 2022Article
- Coastal Waste Detection Based on Deep Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2021Article
- The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods.Sensors (Basel, Switzerland) · 2021Review
- Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy.Entropy (Basel, Switzerland) · 2021Article
- Social Innovation for Food Security and Tourism Poverty Alleviation: Some Examples From China.Frontiers in psychology · 2021Review
- Deep-Stacking Network Approach by Multisource Data Mining for Hazardous Risk Identification in IoT-Based Intelligent Food Management Systems.Computational intelligence and neuroscience · 2021Article
- An Adaptive Filter for Nonlinear Multi-Sensor Systems with Heavy-Tailed Noise.Sensors (Basel, Switzerland) · 2020Article
- Detecting Coal Pulverizing System Anomaly Using a Gated Recurrent Unit and Clustering.Sensors (Basel, Switzerland) · 2020Article
- Reflections and Methodological Proposals to Treat the Concept of "Information Precision" in Smart Agriculture Practices.Sensors (Basel, Switzerland) · 2020Article
- Convolutional Neural Networks for Image-Based Corn Kernel Detection and Counting.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
6 authors.
Funding
Abstract
Smart agricultural sensing has enabled great advantages in practical applications recently, making it one of the most important and valuable systems. For outdoor plantation farms, the prediction of climate data, such as temperature, wind speed, and humidity, enables the planning and control of agricultural production to improve the yield and quality of crops. However, it is not easy to accurately predict climate trends because the sensing data are complex, nonlinear, and contain multiple components. This study proposes a hybrid deep learning predictor, in which an empirical mode decomposition (EMD) method is used to decompose the climate data into fixed component groups with different frequency characteristics, then a gated recurrent unit (GRU) network is trained for each group as the sub-predictor, and finally the results from the GRU are added to obtain the prediction result. Experiments based on climate data from an agricultural Internet of Things (IoT) system verify the development of the proposed model. The prediction results show that the proposed predictor can obtain more accurate predictions of temperature, wind speed, and humidity data to meet the needs of precision agricultural production.
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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.