Ingeniería en Sistemas, Electrónica e Industrial

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Subject: search.filters.subject.AGRICULTURA DE PRECISIÓN

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    Sistema IoT de riego para el cultivo de tomate riñón en la zona del canal de Salcedo
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2025-02) Chimba Amaya Cristian Orlando; Pallo Noroña Juan Pablo
    In this research work, an automated system for irrigation control and monitoring in tomato greenhouse farming was implemented, addressing the need to optimize water use in agriculture through IoT technology. This project was developed in response to the increasing water scarcity and the aim to improve the productivity of tomato crops in the Zona del Canal of the canton Salcedo. The system uses a server hosted on database MART to manage the developed data, integrating sensors to measure variables such as soil moisture and temperature, along with actuators like solenoid valves that regulate water dosing through a drip irrigation system. Additionally, it includes a Node-RED dashboard for real-time monitoring and automated decision-making. The implementation focused on testing different water doses according to the phenological stage of the tomato plants to optimize growth and quality. Functionality tests of the irrigation system were carried out, achieving uniform water distribution due to effective soil moisture control. Tests conducted in the tomato greenhouse indicated that the system is beneficial, as proper water dosage control prevents diseases related to water deficiency or excess, such as botrytis and root rot. As a result, the plants achieved optimal growth, leading to higher tomato production.
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    Sistema electrónico para el monitoreo y control de variables agrícolas empleando los principios de smart farming y agricultura de precisión
    (Universidad Técnica de Ambato. Facultad de Ingeiería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2025-02) Chato Guangasi Henry Paul; Córdova Córdova Edgar Patricio
    In contemporary society, agriculture plays a pivotal role. Although it is predominantly cultivated in the conventional manner, which is outdoors, there has been a notable increase in the cultivation of crops in controlled environments, such as greenhouses. This shift is driven by the need to safeguard plantations from the adverse effects of abrupt climate changes. Moreover, the integration of advanced technology tools has enabled enhanced control over the soil in which the plantations are situated, a practice known as precision agriculture. In this context, a study was conducted with the primary objective of implementing a system to control and monitor agricultural variables using Precision Agriculture and Smart Farming principles. It is imperative to have soil conducive to successful harvesting, as this is directly linked to achieving higher production and quality. The system is founded on the implementation of LoRaWAN technology, a system capable of managing multiple nodes with a high degree of reliability and without the loss of any information. The system is comprised of four distinct stages: data acquisition, transmission, control and processing, and visualization. The acquisition stage involves the use of sensors to gather data from the soil and the environment within the greenhouse, with a demonstrated reliability of 98.1%.The transmission stage employs LoRaWAN technology, utilizing Heltec LoRa32 microcontrollers and a gateway that functions as a central receiver for data from all nodes. The processing and visualization stage employs a dedicated graphical interface, facilitating the observation of measured variables through time-series graphs. In the control stage, the actuators demonstrated high efficiency, responding promptly and accurately to the programmed instructions
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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 estimación de producción de cultivo de mora empleando visión artificial y machine learning
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2025-02) Aldaz Saca Fabricio Javier; Ibarra Rojano Gilber Andrés; Córdova Córdova Edgar Patricio
    Blackberry cultivation is a significant sector of the national economy due to its high demand at both local and international levels. However, growers face considerable challenges in attempting to accurately predict production. Conventional methods, which rely heavily on manual procedures, are often inaccurate and prone to human error, potentially compromising planning and resulting in economic losses. The system developed in this research is composed of four fundamental stages: acquisition, processing, training, and analysis. For data collection, a DJI Mini 2 unmanned aerial vehicle (UAV) was utilized, capable of capturing real-time images of the crops. During processing, these images were analyzed using computer vision techniques, employing tools such as OpenCV and the YOLOv8m detection model. The model was trained with a specific dataset that included photographs of blackberries at different stages of maturity: green, red, and purple. To project potential losses due to factors such as pests, adverse weather, and drought, a Monte Carlo-based simulation was integrated. The results were presented in a graphical interface designed to facilitate visualization and analysis, assisting growers in optimizing their decision-making. The system exhibited 80.55% reliability in blackberry identification and counting, with data captured directly in the field. Furthermore, the simulations yielded a comprehensive evaluation of potential adverse scenarios, enabling more precise and realistic estimates.
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    Sistema cuantificador de calidad de cultivo de manzana para monitoreo de la producción utilizando algoritmos de Aprendizaje Profundo con Visión Artificial y Segmentación de Instancias
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Maestría en Electrónica y Automatización, 2022) Garcés Cadena, Andrés Alejandro; Prado Romo, Álvaro Javier
    Nowadays, agriculture is an activity of marked influence in the economy world, therefore, in order to satisfy the progressive food needs, human beings have been introducing technological tools for the optimization of agricultural practices, this management is also known as Precision Agriculture (PA) Artificial Vision is a technology that has given greater support to Precision Agriculture (PA), granting a wide range of tools with the ability to reduce difficulties faced by the farmer during his hand labor. The aim of this project is to provide farmers a tool to improve the process for apple harvest management, by using Deep Learning (DL) algorithms and a Computer Vision system. The system development includes two study analyses: apple type detection and quality quantification for its inspection and validation using a non-invasive method. For apple type detection, SSD-MobileNet model was used and for apple quality segmentation, a fully convolutional network FCN-ResNet-18 was used. For both studies, networks were retrained with customized databases generated specifically for the development of this project. Lastly, evaluation parameters of the detection and segmentation systems are presented with metrics such as confusion matrices, and overlapping of objects on the IoU, respectively.