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Precision agriculture with AI-based responsive monitoring algorithm

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  • Dusadeerungsikul, Puwadol Oak
  • Nof, Shimon Y.

Abstract

Precision Agriculture (PA) is a relatively new farming approach, applying science and technology to enhance cost-effectiveness and improve food security by optimizing agricultural practices through the treatment of each crop individually. To support the new practice, an AI-based, responsive monitoring algorithm, called the Dynamic-Adaptive Search algorithm, has been developed to minimize operation costs with the benefit of acquiring new and timely information. Three modules of the algorithm are 1) Module for image processing based on AI, 2) Module for error-responsive search expansion, and 3) Module for estimating stress propagation. Computational experiments have demonstrated that the newly developed algorithm outperforms other alternatives, yielding significantly higher system performance and system gain, compared to other algorithms. The sensitivity analysis confirms the algorithm's ability to deliver within ± 10% of the theoretical optimal value, resulting in economic benefits under varying conditions. The algorithm's applications can be extended to other decision-making situations involving cost-benefit tradeoffs of acquiring more data.

Suggested Citation

  • Dusadeerungsikul, Puwadol Oak & Nof, Shimon Y., 2024. "Precision agriculture with AI-based responsive monitoring algorithm," International Journal of Production Economics, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:proeco:v:271:y:2024:i:c:s0925527324000616
    DOI: 10.1016/j.ijpe.2024.109204
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    References listed on IDEAS

    as
    1. Sarkar, Biswajit & Saren, Sharmila, 2016. "Product inspection policy for an imperfect production system with inspection errors and warranty cost," European Journal of Operational Research, Elsevier, vol. 248(1), pages 263-271.
    2. Win P. V. Nguyen & Puwadol Oak Dusadeerungsikul & Shimon Y. Nof, 2022. "Plant stress propagation detection and monitoring with disruption propagation network modelling and Bayesian network inference," International Journal of Production Research, Taylor & Francis Journals, vol. 60(2), pages 723-741, January.
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    5. Naoufel Cheikhrouhou & Biswajit Sarkar & Baishakhi Ganguly & Asif Iqbal Malik & Rafael Batista & Young Hae Lee, 2018. "Optimization of sample size and order size in an inventory model with quality inspection and return of defective items," Annals of Operations Research, Springer, vol. 271(2), pages 445-467, December.
    6. Itoh, Takeshi & Ishii, Hiroaki & Nanseki, Teruaki, 2003. "A model of crop planning under uncertainty in agricultural management," International Journal of Production Economics, Elsevier, vol. 81(1), pages 555-558, January.
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