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Plants have always played a vital role in the lives of all living creatures. In plants, leaves contain stomata, They are responsible for regulating the exchange of gases and water vapor between the plant and its surrounding ecosystem. The irregular behavior of stomata opening and closing exerts a significant influence on plant growth and the functioning of ecosystems. Each stomata is surrounded by two specialized cells, known as guard cells, which control the opening and closing of the stomatal pores. The development of AI-based application requires the use of advanced computer vision tools such as YOLO, which can detect multiple stomata pores on a leaf in real time. This tool can extract useful information from digital microscopic images with a confidence score of 80 to 90%. Its effectiveness further enhance with CNN. Therefore, stomatal dynamics alone are not sufficient for reliable AI-based crop monitoring, and other factors must be considered. However, by combing stomatal behavior with continuous detection of plant conditions such as leaf color, thermal imaging and soil moisture, AI-generative systems can provide more accurate stress assessment and actionable insights for precision agriculture.

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Prediction of Stomatal Dynamics: Leveraging Generative AI for Automated Detection and Analysis of Stomatal Closure

  • 会议论文

  • 发表日期：

    2026年

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  • Zahida Mashaallah,
  • Egidia Cirillo,
  • Alessandro Del Prete

摘要

Plants have always played a vital role in the lives of all living creatures. In plants, leaves contain stomata, They are responsible for regulating the exchange of gases and water vapor between the plant and its surrounding ecosystem. The irregular behavior of stomata opening and closing exerts a significant influence on plant growth and the functioning of ecosystems. Each stomata is surrounded by two specialized cells, known as guard cells, which control the opening and closing of the stomatal pores. The development of AI-based application requires the use of advanced computer vision tools such as YOLO, which can detect multiple stomata pores on a leaf in real time. This tool can extract useful information from digital microscopic images with a confidence score of 80 to 90%. Its effectiveness further enhance with CNN. Therefore, stomatal dynamics alone are not sufficient for reliable AI-based crop monitoring, and other factors must be considered. However, by combing stomatal behavior with continuous detection of plant conditions such as leaf color, thermal imaging and soil moisture, AI-generative systems can provide more accurate stress assessment and actionable insights for precision agriculture.