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    VPD Monitoring with ESP32 and Flask API for Early Detection of Powdery Mildew in Rose Greenhouses
    (2026)
    Herrera, Vicente-D.
    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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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    Comparative Analysis of Neural Networks and Data Processing Techniques for Parkinson’s Gait Classification
    (2024)
    Israel Reyes
    ;
    Francis Andaluz
    ;
    Kerly Troya
    ;
    Luis Zhinin-Vera
    ;
    Diego Almeida-Galárraga
    Parkinson’s disease (PD) is an advancing neurodegenerative condition characterized by motor symptoms, including disturbances in gait and varying degrees of severity, typically assessed using the Hoehn and Yahr stages. Precise classification of PD gait patterns and severity levels is of paramount importance for efficient diagnosis and continuous treatment monitoring. This research article presents a comprehensive assessment of the performance of three distinct Artificial Neural Network (ANN) models integrated with diverse data processing techniques, encompassing segmentation, filtration, and noise reduction, in the context of classifying PD severity. The classification is based on the vertical ground reaction force (VGRF) measurements obtained from both healthy individuals and those afflicted by Parkinson’s disease, sourced from a well-established database (GaitPDB, Physio Net). The study provides a comparative analysis of the efficacy of these models in accurately discriminating between various gait patterns and stages of disease severity, underscoring their potential to enhance clinical decision-making and patient care. Additionally, the study offers valuable insights into the impact of data processing methodologies on classification performance
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