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Smart Irrigation for Coriander Plant: Saving Water with AI and IoT

Author

Listed:
  • Abhirup Paria

    (Haldia Institute of Technology
    Jadavpur University)

  • Arindam Giri

    (Haldia Institute of Technology)

  • Subrata Dutta

    (National Institute of Technology)

  • Sarmistha Neogy

    (Jadavpur University)

Abstract

Accurate forecasting of water requirements is crucial for optimizing irrigation and water preservation. This paper presents a real-time intelligent irrigation system for the various growth stages of coriander plants, utilizing Internet of Things (IoT) sensors and hybrid machine learning (ML) models optimized with a genetic algorithm (GA). A novel method is introduced for the first time to estimate net solar radiation based on sunshine duration data collected via the BH1750 sensor, helping to calculate evapotranspiration ( $$EV_{T0}$$ ) for precise crop-specific water requirements. Based on the selection, mutation and crossover operators of GA, nine hybrid artificial neural network (ANN) models are developed to predict $$EV_{T0}$$ . It may be mentioned here that hybrid machine learning model 4 (HML4) showed the best performance with $$R^2$$ 0.98 among the nine hybrid models evaluated. Furthermore, the exact water requirements are determined for each growth stage of the coriander plants using the predicted $$EV_{T0}$$ and crop-coefficient ( $$C_C$$ ). To enhance the applicability of the proposed system, an Android application is designed and implemented for the remote monitoring and management of intelligent irrigation system, demonstrating its effectiveness in optimizing irrigation practices. The proposed intelligent system can significantly minimize flood irrigation, water consumption, and labour expenses showing a new direction in smart agriculture.

Suggested Citation

  • Abhirup Paria & Arindam Giri & Subrata Dutta & Sarmistha Neogy, 2025. "Smart Irrigation for Coriander Plant: Saving Water with AI and IoT," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(7), pages 3379-3395, May.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:7:d:10.1007_s11269-025-04112-x
    DOI: 10.1007/s11269-025-04112-x
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    References listed on IDEAS

    as
    1. Jayashree T R & NV Subba Reddy & U Dinesh Acharya, 2023. "Modeling Daily Reference Evapotranspiration from Climate Variables: Assessment of Bagging and Boosting Regression Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1013-1032, February.
    2. Zhang, Lei & Zhao, Xin & Zhu, Ge & He, Jun & Chen, Jian & Chen, Zhicheng & Traore, Seydou & Liu, Junguo & Singh, Vijay P., 2023. "Short-term daily reference evapotranspiration forecasting using temperature-based deep learning models in different climate zones in China," Agricultural Water Management, Elsevier, vol. 289(C).
    3. Fatemeh Javanbakht-Sheikhahmad & Farahnaz Rostami & Hossein Azadi & Hadi Veisi & Farzad Amiri & Frank Witlox, 2024. "Agricultural Water Resource Management in the Socio-Hydrology: A Framework for Using System Dynamics Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(8), pages 2753-2772, June.
    4. Dong, Juan & Xing, Liwen & Cui, Ningbo & Guo, Li & Liang, Chuan & Zhao, Lu & Wang, Zhihui & Gong, Daozhi, 2024. "Estimating reference crop evapotranspiration using optimized empirical methods with a novel improved Grey Wolf Algorithm in four climatic regions of China," Agricultural Water Management, Elsevier, vol. 291(C).
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