Next Article in Journal
Consumers’ Perception and Willingness to Pay for Eco-Labeled Seafood in Italian Hypermarkets
Previous Article in Journal
Evaluating the Contribution of Soybean Rust- Resistant Cultivars to Soybean Production and the Soybean Market in Brazil: A Supply and Demand Model Analysis
Previous Article in Special Issue
How Are Information Technologies Addressing Broiler Welfare? A Systematic Review Based on the Welfare Quality® Assessment
Open AccessArticle

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

by Xue-Bo Jin 1,2,3, Xing-Hong Yu 1,2,3, Xiao-Yi Wang 1,2,3,*, Yu-Ting Bai 1,2,3, Ting-Li Su 1,2,3 and Jian-Lei Kong 1,2,3
1
School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
2
China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China
3
Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(4), 1433; https://doi.org/10.3390/su12041433
Received: 20 January 2020 / Revised: 4 February 2020 / Accepted: 12 February 2020 / Published: 14 February 2020
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.
Keywords: deep learning predictor; GRU; precision agriculture; IoT; sequential two-level decomposition structure; medium- and long-term prediction deep learning predictor; GRU; precision agriculture; IoT; sequential two-level decomposition structure; medium- and long-term prediction
MDPI and ACS Style

Jin, X.-B.; Yu, X.-H.; Wang, X.-Y.; Bai, Y.-T.; Su, T.-L.; Kong, J.-L. Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System. Sustainability 2020, 12, 1433.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop