Tesis Telecomunicaciones

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    Sistema de monitoreo y control IOTpara cultivos agrícolas basado en la arquitectura Edge Cloud y Deep Learning
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2025-02) Amán Caiza Byron Alexander; Manzano Villafuerte Víctor Santiago
    Low agricultural productivity is a significant challenge that impacts the efficiency and sustainability of the sector, especially in developing countries. In the case of hydroponic crops, this problem is aggravated by the lack of advanced monitoring and control systems. To address this limitation, the IoT Monitoring and Control System for Agricultural Crops based on Edge-Cloud and Deep Learning was developed, optimizing crop growth and management. The implementation of the IoT system for monitoring and control of agricultural crops based on Edge-Cloud and Deep Learning architectures allowed for real-time data processing, optimizing resource management and reducing manual intervention. It was developed in four layers. In the Edge Layer, devices collected pH and electrical conductivity data, while a camera captured the lettuce. In the Server Layer, data was processed and stored, the artificial intelligence model was trained and applied. In the Cloud Layer, a virtual network managed the information. Finally, in the Display Layer, an interface allowed real-time visualization, facilitating system monitoring. The results demonstrated the effectiveness of the system, reaching an accuracy of 99.76% and 98.80% in the measurement of pH and electrical conductivity. The lettuce disease recognition model achieved 87% in training and 84.92% in real tests, allowing early detection and reducing losses. The integration of IoT, Edge-Cloud and Deep Learning optimized monitoring and control, reducing costs and improving efficiency in the application of nutrients, guaranteeing a more sustainable system.