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In this study, a vapor pressure deficit (VPD) monitoring system was developed using an ESP32 and a Flask-based API for the early detection of powdery mildew in rose greenhouses. The research demonstrated that the integration of intelligent systems and real-time analysis of environmental conditions allows for the rapid identification of factors that favor the development of powdery mildew. The SVM model employed achieved high accuracy, with a classification accuracy rate of 96% in identifying conditions conducive to this disease, which is crucial for reducing unnecessary interventions and optimizing resource management in greenhouses. Additionally, the use of an API facilitates the integration of the system with other management platforms, enhancing data accessibility and decision-making. This approach promotes more responsible agricultural practices aligned with environmental sustainability.

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VPD Monitoring with ESP32 and Flask API for Early Detection of Powdery Mildew in Rose Greenhouses

  • 会议论文

  • 发表日期：

    2026年

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  • Vicente-D. Herrera,
  • David-I. Ilvis,
  • Michelle-C. Herrera,
  • Jessica-C. Mora,
  • Christian-S. Jacho,
  • Noemi Arcentales,
  • Alex-D. Vega,
  • Juan Escobar-Naranjo,
  • Nicole-L. Molina,
  • Miguel-D. Escudero,
  • Alex Maigua-Quinteros

摘要

In this study, a vapor pressure deficit (VPD) monitoring system was developed using an ESP32 and a Flask-based API for the early detection of powdery mildew in rose greenhouses. The research demonstrated that the integration of intelligent systems and real-time analysis of environmental conditions allows for the rapid identification of factors that favor the development of powdery mildew. The SVM model employed achieved high accuracy, with a classification accuracy rate of 96% in identifying conditions conducive to this disease, which is crucial for reducing unnecessary interventions and optimizing resource management in greenhouses. Additionally, the use of an API facilitates the integration of the system with other management platforms, enhancing data accessibility and decision-making. This approach promotes more responsible agricultural practices aligned with environmental sustainability.