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Research on Intelligent Decision-Making Irrigation Model of Water and Fertilizer Based on Multi-source Data Input

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Artificial Intelligence (CICAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13605))

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Abstract

At present, the integrated irrigation management and control system of water and fertilizer has met the requirements of automatic control of farmland water and fertilizer, gradually transforming the traditional manual operation into facility industrialization. However, this method has a weak use of data, and there is still a large gap between the calculation method and intelligent management and control. Taking greenhouse cabbage as the main research object, based on the cultivation environmental parameters, growth morphological parameters, water and fertilizer irrigation requirements during the growth period of cabbage, and using the efficient allocation ability of attention mechanism to data feature weights, this paper proposes the establishment of water and fertilizer intelligent decision-making management and control model integrating multi-source data input. The results showed that the prediction error of the intelligent decision-making irrigation model for water and fertilizer for greenhouse cabbage was relatively small, RMSE was 0.002447 m3/Day, MAE is 0.001779 m3/Day, and the coupling relationship between multi-source data is comprehensively analyzed, and the overall performance of model decision-making is improved through multi-feature extraction.

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Acknowledgements

This work was supported by Innovation 2030 Major S&T Projects of China (2021ZD0113604) and China Agriculture Research System of MOF and MARA (CARS-23-D07).

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Authors and Affiliations

  1. National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China

    Shanshan Li, Yisheng Miao, Xiao Han & Wei Guo

  2. Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China

    Shanshan Li, Yisheng Miao, Xiao Han & Wei Guo

  3. Key Laboratory of Digital Village Technology, Ministry of Agriculture and Rural Affairs, People’s Republic of China, Beijing, 100097, China

    Shanshan Li, Yisheng Miao, Xiao Han & Wei Guo

Authors
  1. Shanshan Li
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  2. Yisheng Miao
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  3. Xiao Han
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  4. Wei Guo
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Corresponding author

Correspondence to Wei Guo .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lu Fang

  2. Xiaomi Inc., Beijing, China

    Daniel Povey

  3. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  4. JD Explore Academy, Beijing, China

    Tao Mei

  5. Chinese Academy of Sciences, Beijing, China

    Ruiping Wang

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Li, S., Miao, Y., Han, X., Guo, W. (2022). Research on Intelligent Decision-Making Irrigation Model of Water and Fertilizer Based on Multi-source Data Input. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13605. Springer, Cham. https://doi.org/10.1007/978-3-031-20500-2_17

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  • DOI: https://doi.org/10.1007/978-3-031-20500-2_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20499-9

  • Online ISBN: 978-3-031-20500-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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