Fusion: Practice and Applications

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Volume 21 , Issue 2 , PP: 369-382, 2026 | Cite this article as | XML | Html | PDF | Full Length Article

Irrigation Iot Sensor Data Analytics Using Bio-Inspired Data Mining Techniques

Maysaa H. Abdulameer 1 * , Saif M. Ali 2 , Deshinta Arrova Dewi 3

  • 1 Collage of nursing, Baghdad University, Baghdad, Iraq - (maysaa.h@conursing.uobaghdad.edu.iq)
  • 2 Department of Computer Science, Dijlah University Collage, Baghdad, Iraq - (saif.alameri@duc.edu.iq)
  • 3 Center for Data Science and Sustainable Technologies, INTI International University, Malaysia - (deshinta.ad@newinti.edu.my)
  • Doi: https://doi.org/10.54216/FPA.210223

    Received: March 28, 2025 Revised: June 19, 2025 Accepted: August 10, 2025
    Abstract

    Recently, irrigation management has been considered one of the most significant areas of research in smart vertical farming. Hence, it is essential to optimize freshwater usage for smart vertical farming management due to the lack of freshwater sources. It is observed that the soil moisture level and temperature data need to be appropriately examined and analyzed to predict the water irrigation level in a smart farming platform. Hence, in this work, the Internet of Things (IoT) sensors have been utilized to collect and monitor the soil moisture level, ambient temperature level, and humidity level data effectively. Besides, the collected sensor information has been analyzed and predicted to recognize the appropriate utilization of the optimum level of freshwater using Grey Wolf optimizer integrated recurrent network models. Therefore, this approach successfully analyzes the sensors' data and predicts the required level of irrigation based on motor ON and OFF conditions. The generated data from the sensor has been evaluated using the Keras model using the python language, and the performance is assessed based on the accuracy ratio. This model obtained a maximum of (0.995%) accuracy in forecasting the optimum irrigation level. The proposed system will utilize less voltage to minimize the power consumption ratio up to 35% in the irrigation process with 99.5% accuracy in forecasting the optimum irrigation level.

    Keywords :

    Irrigation , Iot , Sensor , Data Analytics , g Bio-Inspired Data Mining

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    Cite This Article As :
    H., Maysaa. , M., Saif. , Arrova, Deshinta. Irrigation Iot Sensor Data Analytics Using Bio-Inspired Data Mining Techniques. Fusion: Practice and Applications, vol. , no. , 2026, pp. 369-382. DOI: https://doi.org/10.54216/FPA.210223
    H., M. M., S. Arrova, D. (2026). Irrigation Iot Sensor Data Analytics Using Bio-Inspired Data Mining Techniques. Fusion: Practice and Applications, (), 369-382. DOI: https://doi.org/10.54216/FPA.210223
    H., Maysaa. M., Saif. Arrova, Deshinta. Irrigation Iot Sensor Data Analytics Using Bio-Inspired Data Mining Techniques. Fusion: Practice and Applications , no. (2026): 369-382. DOI: https://doi.org/10.54216/FPA.210223
    H., M. , M., S. , Arrova, D. (2026) . Irrigation Iot Sensor Data Analytics Using Bio-Inspired Data Mining Techniques. Fusion: Practice and Applications , () , 369-382 . DOI: https://doi.org/10.54216/FPA.210223
    H. M. , M. S. , Arrova D. [2026]. Irrigation Iot Sensor Data Analytics Using Bio-Inspired Data Mining Techniques. Fusion: Practice and Applications. (): 369-382. DOI: https://doi.org/10.54216/FPA.210223
    H., M. M., S. Arrova, D. "Irrigation Iot Sensor Data Analytics Using Bio-Inspired Data Mining Techniques," Fusion: Practice and Applications, vol. , no. , pp. 369-382, 2026. DOI: https://doi.org/10.54216/FPA.210223