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Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning

Author

Listed:
  • Angelin Blessy

    (School of Computer Science and Engineering, Galgotias Univeristy, Greater Noida 201310, India)

  • Avneesh Kumar

    (School of Computer Science and Engineering, Galgotias Univeristy, Greater Noida 201310, India)

  • Prabagaran A

    (Visa Consolidated Support Services India Pvt. Ltd., Bangalore 560048, India)

  • Abdul Quadir Md

    (School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India)

  • Abdullah I. Alharbi

    (Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh 21911, Saudi Arabia)

  • Ahlam Almusharraf

    (Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Surbhi B. Khan

    (Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 36/S-12, Lebanon
    Department of Data Science, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK)

Abstract

Irrigation systems are a crucial research area because it is essential to conserve fresh water and utilize it wisely. As a part of this study, the reliability of predicting the usage of water in the present and future is investigated in order to develop an effective prediction model to communicate demand. In order to improve prediction, we develop a prediction model and share the updated model with nearby farmers. In order to forecast the irrigation requirements, the recommended model utilizes the Internet of Things (IoT), k-nearest neighbours (KNN), cloud storage, long short-term memory (LSTM), and adaptive network fuzzy inference system (ANFIS) techniques. By collecting real-time environmental data, KNN identifies the closest water requirement from the roots and its surrounding. In order to predict short-term requirements, ANFIS is used. To transfer the new requirements for better prediction, transfer learning is used. Time-series-data updates are predicted using LSTM for future forecasting, and the integrated model is shared with other farmers using cloud environments to enhance forecasting and analysis. For implementation, a period of nine to ten months of data was collected from February to December 2021, and banana tree was used to implement the planned strategy. Four farms, with measurements, were considered at varying intervals to determine the minimum and maximum irrigation needs. The requirements of farms were collected over time and compared to the predictions. Future requirements at 8, 16, 24, 32, and 48 h were also anticipated. The results indicated were compared to manual water pouring, and, thus, the entire crop used less water, making our prediction model a real-world option for irrigation. The prediction model was evaluated using R 2 , MSLE and the average initial prediction value of R 2 was 0.945. After using transfer learning, the prediction of the model of Farm-2, 3 and 4 were 0.951, 0.958 and 0.967, respectively.

Suggested Citation

  • Angelin Blessy & Avneesh Kumar & Prabagaran A & Abdul Quadir Md & Abdullah I. Alharbi & Ahlam Almusharraf & Surbhi B. Khan, 2023. "Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8260-:d:1150518
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    References listed on IDEAS

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    1. Olutobi Adeyemi & Ivan Grove & Sven Peets & Tomas Norton, 2017. "Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation," Sustainability, MDPI, vol. 9(3), pages 1-29, February.
    2. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    3. Mahmoudi, Neda & Majidi, Arash & Jamei, Mehdi & Jalali, Mohammadnabi & Maroufpoor, Saman & Shiri, Jalal & Yaseen, Zaher Mundher, 2022. "Mutating fuzzy logic model with various rigorous meta-heuristic algorithms for soil moisture content estimation," Agricultural Water Management, Elsevier, vol. 261(C).
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    2. Kelum Sandamal & Sachini Shashiprabha & Nitin Muttil & Upaka Rathnayake, 2023. "Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance," Sustainability, MDPI, vol. 15(12), pages 1-17, June.

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