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

Xue-Bo Jin, Wei-Zhen Zheng, Jian-Lei Kong, Xiao-Yi Wang, Min Zuo, Qing-Chuan Zhang and Seng Lin
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Xue-Bo Jin: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Wei-Zhen Zheng: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Jian-Lei Kong: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Xiao-Yi Wang: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Min Zuo: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Qing-Chuan Zhang: School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Seng Lin: Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China

Agriculture, 2021, vol. 11, issue 8, 1-25

Abstract: 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 temporal data, this study proposes a bidirectional self-attentive encoder–decoder framework (BEDA) to construct the long-time predictor for multiple environmental factors with high nonlinearity and noise in a smart greenhouse. Firstly, the original data are denoised by wavelet threshold filter and pretreatment operations. Secondly, the bidirectional long short-term-memory is selected as the fundamental unit to extract time-serial features. Then, the multi-head self-attention mechanism is incorporated into the encoder–decoder framework to improve the prediction performance. Experimental investigations are conducted in a practical greenhouse to accurately predict indoor environmental factors (temperature, humidity, and CO 2 ) from noisy IoT-based sensors. The best model for all datasets was the proposed BEDA method, with the root mean square error of three factors’ prediction reduced to 2.726, 3.621, and 49.817, and with an R of 0.749 for temperature, 0.848 for humidity, and 0.8711 for CO 2 concentration, respectively. The experimental results show that the favorable prediction accuracy, robustness, and generalization of the proposed method make it suitable to more precisely manage greenhouses.

Keywords: intelligent agricultural greenhouse; environmental factor prediction; deep-learning encoder–decoder; self-attention mechanism; Internet of Things (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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