Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System

Sustainability

卷:12

Xue-Bo Jin¹  Jian-Lei Kong¹  Xiao-Yi Wang¹  Ting-Li Su¹  Yu-Ting Bai¹  Xing-Hong Yu¹ 

[1] School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China;

关键词: deep learning predictor;    gru;    precision agriculture;    iot;    sequential two-level decomposition structure;    medium- and long-term prediction;   

DOI  :  10.3390/su12041433

来源: DOAJ

【 摘 要 】

Based on the collected weather data from the agricultural Internet of Things (IoT) system, changes in the weather can be obtained in advance, which is an effective way to plan and control sustainable agricultural production. However, it is not easy to accurately predict the future trend because the data always contain complex nonlinear relationship with multiple components. To increase the prediction performance of the weather data in the precision agriculture IoT system, this study used a deep learning predictor with sequential two-level decomposition structure, in which the weather data were decomposed into four components serially, then the gated recurrent unit (GRU) networks were trained as the sub-predictors for each component. Finally, the results from GRUs were combined to obtain the medium- and long-term prediction result. The experiments were verified for the proposed model based on weather data from the IoT system in Ningxia, China, for wolfberry planting, in which the prediction results showed that the proposed predictor can obtain the accurate prediction of temperature and humidity and meet the needs of precision agricultural production.

【 授权许可】

Unknown