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A smart IoT-based irrigation system design using AI and prediction model

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Abstract

Implementing intelligent irrigation and adjusting the irrigation system is essential in today’s agricultural system to control the amount of water required for the plant. This study focuses on data obtained from sensors measuring (soil temperature and humidity, temperature and humidity of ambient and light) and image processing of plant leaves. To analyze the data, two models were implemented including a regression model in SPSS software and another model by genetic programming in MATLAB 2018 software. The optimal model was a combined model of sensors and images in genetic programming with higher R2 and lower standard error of 0.88 and 0.03, respectively. This model was superior to the regression model which had an R2 and standard error of 0.86 and 0.21, respectively, so this optimal model was selected to adjust the microcontroller for the intelligent irrigation system. The following year by replanting the crop, the intelligent irrigation system was presented as the superior system with 11% water saving compared to the previous year (irrigation by the user). Also, no changes were observed in the yield and color indicators of the plant at the level of 5%, which indicates the superiority of the intelligent irrigation system and its high accuracy of this system.

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Data availability

The data supporting the findings of this study are available upon reasonable request from the corresponding authors.

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Acknowledgements

The present investigation forms an integral component of a doctoral thesis conducted at the Department of Agricultural Machinery Engineering, situated within the esteemed Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran. The authors of this study express their gratitude towards the university for providing financial assistance towards the completion of this research. Additionally, we extend our sincere appreciation to the anonymous reviewer for their valuable comments and insightful suggestions on the manuscript.

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Authors and Affiliations

  1. Department of Agricultural Machinery and Mechanization, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Khuzestan, Iran

    Faeze Behzadipour, Mahmod Ghasemi Nezhad Raeini, Saman Abdanan Mehdizadeh & Morteza Taki

  2. Department of Soil Sciences, Faculty of Agricultural Sciences, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran

    Bijan Khalil Moghadam

  3. Department of Horticultural Sciences, Faculty of Agricultural Sciences, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran

    Mohammad Reza Zare Bavani

  4. Instituto de Investigación Para La Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, 46730, Grau de Gandia, Spain

    Jaime Lloret

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  1. Faeze Behzadipour
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Correspondence to Saman Abdanan Mehdizadeh.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This work was supported by the Agricultural Sciences and Natural Resources University of Khuzestan.

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Behzadipour, F., Ghasemi Nezhad Raeini, M., Abdanan Mehdizadeh, S. et al. A smart IoT-based irrigation system design using AI and prediction model. Neural Comput & Applic 35, 24843–24857 (2023). https://doi.org/10.1007/s00521-023-08987-y

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