Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse

Smart agricultural greenhouses provide well-controlled conditions for crop cultivation but require accurate prediction of environmental factors to ensure ideal crop growth and management efficiency. Due to the limitations of existing predictors in dealing with massive, nonlinear, and dynamic tempora...

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Veröffentlicht in:	Agriculture (Basel) Jg. 11; H. 8; S. 802
Hauptverfasser:	Jin, Xue-Bo, Zheng, Wei-Zhen, Kong, Jian-Lei, Wang, Xiao-Yi, Zuo, Min, Zhang, Qing-Chuan, Lin, Seng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.08.2021
Schlagworte:	Accuracy Agricultural production agriculture Algorithms Carbon dioxide Coders Controlled conditions Crop growth data collection Deep learning deep-learning encoder–decoder environmental factor prediction Environmental factors Farm buildings Feature extraction Greenhouses Humidity Indoor environments intelligent agricultural greenhouse Internet of Things Mathematical models Microclimate Noise Nonlinear systems Nonlinearity Parameter estimation Photosynthesis prediction Predictions Principal components analysis Radiation self-attention mechanism temperature Time series wavelet
ISSN:	2077-0472, 2077-0472
Online-Zugang:	Volltext
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