Tesis Telecomunicaciones

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    Sistema para la caracterización de enfermedades de cultivos de cebolla mediante el uso de procesamiento digital de imágenes
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2025-02) Hidalgo Segovia Oscar Gustavo; Guamán Molina Jesús Israel
    Modern agriculture faces critical challenges related to disease detection and management in high-value crops such as onion, an essential commodity with an annual global production of approximately 300 million tons. This project proposes an innovative system for disease characterization in onion crops using digital image processing, IoT and artificial intelligence. The system integrates sensors to collect key environmental data, such as temperature, humidity and UV radiation, which are stored in a MongoDB database. This data is visualized with Chart.js, complementing digital image analysis to detect diseases such as Botrytis squamosa and powdery mildew. Through advanced deep learning algorithms, such as YOLOv8, the system identifies visual patterns associated with diseases, enabling early disease detection. The implementation of the system comprises four stages: data acquisition through sensors and cameras, preprocessing using OpenCV, training of models based on neural networks, and visualization of results through graphical interfaces. This integrated approach not only optimizes disease detection and management, but also improves efficiency in agricultural decision making, reducing losses and maximizing productivity. Thus, it contributes significantly to the sustainability of the agricultural sector, especially in regions with low technification such as Tungurahua, Ecuador
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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.