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

Author

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  • Xue-Bo Jin

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China)

  • Xing-Hong Yu

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China)

  • Xiao-Yi Wang

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China)

  • Yu-Ting Bai

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China)

  • Ting-Li Su

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China)

  • Jian-Lei Kong

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China)

Abstract

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.

Suggested Citation

  • Xue-Bo Jin & Xing-Hong Yu & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Jian-Lei Kong, 2020. "Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1433-:d:320873
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    References listed on IDEAS

    as
    1. Yuting Bai & Xuebo Jin & Xiaoyi Wang & Tingli Su & Jianlei Kong & Yutian Lu, 2019. "Compound Autoregressive Network for Prediction of Multivariate Time Series," Complexity, Hindawi, vol. 2019, pages 1-11, September.
    2. Xinghan Xu & Weijie Ren, 2019. "Application of a Hybrid Model Based on Echo State Network and Improved Particle Swarm Optimization in PM 2.5 Concentration Forecasting: A Case Study of Beijing, China," Sustainability, MDPI, vol. 11(11), pages 1-19, May.
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    Cited by:

    1. Huo, Dongyang & Malik, Asad Waqar & Ravana, Sri Devi & Rahman, Anis Ur & Ahmedy, Ismail, 2024. "Mapping smart farming: Addressing agricultural challenges in data-driven era," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    2. Muhammad Fahad & Tariq Javid & Hira Beenish & Adnan Ahmed Siddiqui & Ghufran Ahmed, 2021. "Extending ONTAgri with Service-Oriented Architecture towards Precision Farming Application," Sustainability, MDPI, vol. 13(17), pages 1-14, August.
    3. Yan Guo & Xiaonan Hu & Zepeng Wang & Wei Tang & Deyu Liu & Yunzhong Luo & Hongxiang Xu, 2021. "The butterfly effect in the price of agricultural products: A multidimensional spatial-temporal association mining," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(11), pages 457-467.
    4. Xue-Bo Jin & Wen-Tao Gong & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su, 2022. "PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data," Mathematics, MDPI, vol. 10(4), pages 1-17, February.
    5. Tao Zhen & Lei Yan & Jian-lei Kong, 2020. "An Acceleration Based Fusion of Multiple Spatiotemporal Networks for Gait Phase Detection," IJERPH, MDPI, vol. 17(16), pages 1-17, August.
    6. Hawon Chu & Jaeseong Kim & Seounghyeon Kim & Young-Kyoon Suh & Ryong Lee & Rae-Young Jang & Minwoo Park, 2020. "ST-Trie: A Novel Indexing Scheme for Efficiently Querying Heterogeneous, Spatiotemporal IoT Data," Sustainability, MDPI, vol. 12(22), pages 1-21, November.
    7. Görkem Giray & Cagatay Catal, 2021. "Design of a Data Management Reference Architecture for Sustainable Agriculture," Sustainability, MDPI, vol. 13(13), pages 1-17, June.
    8. Krzysztof Lalik & Jakub Kozak & Szymon Podlasek & Mateusz Kozek, 2022. "Self-Powered Wireless Sensor Matrix for Air Pollution Detection with a Neural Predictor," Energies, MDPI, vol. 15(6), pages 1-26, March.
    9. Alessandro Scuderi & Giovanni La Via & Giuseppe Timpanaro & Luisa Sturiale, 2022. "The Digital Applications of “Agriculture 4.0”: Strategic Opportunity for the Development of the Italian Citrus Chain," Agriculture, MDPI, vol. 12(3), pages 1-13, March.
    10. Ruiqing Wang & Jinlei Feng & Wu Zhang & Bo Liu & Tao Wang & Chenlu Zhang & Shaoxiang Xu & Lifu Zhang & Guanpeng Zuo & Yixi Lv & Zhe Zheng & Yu Hong & Xiuqi Wang, 2023. "Detection and Correction of Abnormal IoT Data from Tea Plantations Based on Deep Learning," Agriculture, MDPI, vol. 13(2), pages 1-20, February.
    11. Yi Yang & Yuting Bai & Xiaoyi Wang & Li Wang & Xuebo Jin & Qian Sun, 2020. "Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes," Sustainability, MDPI, vol. 12(4), pages 1-16, February.

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