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Study on the Intelligent Control Model of a Greenhouse Flower Growing Environment

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Data Science (ICPCSEE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1629))

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Abstract

Intelligent control of the greenhouse planting environment plays an important role in improving planting efficiency and guaranteeing the quality of precious flowers. Among them, how to adapt the air humidity, temperature and light intensity in greenhouses to the different needs of the flower growth cycle is the key problem of intelligent control. Therefore, an intelligent flower planting environment monitoring and control system model (named) based on the Internet of Things and fuzzy-GRU network adaptive learning is proposed. The above three parameters in the greenhouse were used as model input parameters. The optimal growth humidity, temperature and illumination intensity of flowers are determined by the model, and the output temperature, humidity and illumination intensity act on the executing organ of the greenhouse room by the single-chip microcomputer. The model was evaluated using field greenhouse crops. The results show that the performance of this model is better than that of the PID model and fuzzy control model in simulation experiments and actual scene control. Compared with the flowers in the natural state, the plants of the flowers under systematic control were approximately 6 cm higher than those in the natural state on average, the blooming time of the flowers was approximately two days longer than that in the natural state, and the quality of the flowers was stable.

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Acknowledgments

This work was supported by the Guangxi Key Research and Development Program [Grant no: AB21196063]; Major Achievement Transformation Foundation of Guilin [Grant No. 20192013-1]; Innovation and Entrepreneurship Training Program for College Students of Guilin University of Electronic Technology [Grant No. 202010595031].

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Authors and Affiliations

  1. School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China

    Jinyang Zhen, Rui Xu & Jian Li

  2. Satellite Navigation Positioning and Location Service Engineering Research Center, Guilin University of Electronic Technology, Guilin, 541004, China

    Shiming Shen

  3. Ecological and Environmental Monitoring Center of Guangxi, Guilin, 541002, China

    Jianhui Wen

Authors
  1. Jinyang Zhen
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  2. Rui Xu
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  3. Jian Li
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  4. Shiming Shen
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  5. Jianhui Wen
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Corresponding author

Correspondence to Shiming Shen .

Editor information

Editors and Affiliations

  1. Southwest Petroleum University, Chengdu, China

    Yang Wang

  2. University of Electronic Science and Technology of China, Chengdu, China

    Guobin Zhu

  3. Harbin Engineering University, Harbin, China

    Qilong Han

  4. Southwest Petroleum University, Chengdu, China

    Liehui Zhang

  5. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  6. National Academy of Guo Ding Institute of Data Sciences, Beijing, China

    Zeguang Lu

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Zhen, J., Xu, R., Li, J., Shen, S., Wen, J. (2022). Study on the Intelligent Control Model of a Greenhouse Flower Growing Environment. In: Wang, Y., Zhu, G., Han, Q., Zhang, L., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2022. Communications in Computer and Information Science, vol 1629. Springer, Singapore. https://doi.org/10.1007/978-981-19-5209-8_9

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  • DOI: https://doi.org/10.1007/978-981-19-5209-8_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-5208-1

  • Online ISBN: 978-981-19-5209-8

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