Curso Superior de Tecnologia em Eletrônica Industrial

URI permanente desta comunidadehttps://ri.ifam.edu.br/handle/4321/957

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Sistema de detecção de Tomates (SDT): uma ferramenta de Gestão para agricultores nas fases de maturação
(2025-04-02) Fernandes, Paulo Sérgio Lima; Martiniano, Alexandre Lopes; http://lattes.cnpq.br/2232239320901259; Souza, Wendisson da Silva; Palhares Júnior, Eduardo; http://lattes.cnpq.br/6704112028750834
This study presents the development and application of a computer vision system for the detection and classification of tomatoes at different ripening stages using the YOLO v8 model. The system was tested in a controlled agricultural environment, where images and videos were analyzed to identify ripe, green, and rotten tomatoes. To facilitate data visualization and interpretation, an interactive statistical dashboard was implemented, enabling real-time monitoring of agricultural production. In addition to fruit detection, the system was integrated with a climate monitoring module, providing information on temperature, humidity, and precipitation to support decision- making processes. The results indicate that the model demonstrated good performance in identifying ripe and rotten tomatoes, but faced challenges in accurately detecting green tomatoes due to lighting variations and the lower representation of this class in the dataset. This research highlights the potential of artificial intelligence in agriculture, fostering greater efficiency, automation, and quality control in food production.
Sistema Integrado de detecção de EPIS - SIDE
(2025-04-11) Prestes, Emyli Beatriz Braga; Martiniano, Alexandre Lopes; http://lattes.cnpq.br/2232239320901259; Ribeiro, João Bernardo Aranha; http://lattes.cnpq.br/9027441032059817; Velozo, Hugo Alves; http://lattes.cnpq.br/8351107136518878
This work presents the development of a computer vision system aimed at the automatic detection of Personal Protective Equipment (PPE) in workplace environments. The proposal seeks to integrate artificial intelligence technology with occupational safety, contributing to accident prevention and compliance with regulatory standards. The system uses the YOLOv8 model, a real-time object detection neural network trained to identify the main PPE items: helmet, safety glasses, face mask, and reflective vest. The dataset annotation and preparation were carried out using the Roboflow platform, which also facilitated the resizing, organization, and diversification of the images. For image capture, a camera connected to a Raspberry Pi was used, sending the data to a graphical interface developed in Python using the Streamlit framework. This interface allows real-time visualization of the detected equipment, making analysis easier for the user. The system was designed to be easy to implement and operate, offering a low-cost solution with potential application in various industrial sectors. Tests showed promising results regarding detection accuracy, even under different lighting conditions. The research highlights the potential of using computer vision and IoT to promote safer and more intelligently monitored workplaces. The combination of accessible hardware, intuitive software, and deep learning models proved effective in building systems that support occupational safety.
STDMC – Sistema de Transmissão de Dados e Monitoramento de Consumo de água: um estudo de caso no Laboratório de Projetos do IFAM-CMDI /
(2024-09-27) Souza, Rander Cardoso de; Martiniano, Alexandre Lopes; http://lattes.cnpq.br/2232239320901259; Martiniano, Alexandre Lopes; http://lattes.cnpq.br/2232239320901259; Silva, Nivaldo Rodrigues e; http://lattes.cnpq.br/9653122662843005; Souza, Wenndisson da Silva; http://lattes.cnpq.br/9258045359598622
This work details the development of a water consumption automation and control system for industrial systems, consisting of a physical prototype and a web platform. The goal is to monitor and optimize water use, providing real-time data for strategic decisions. The physical prototype is the basis of the system. It brings together a water flow sensor, an ESP8266 microcontroller, and a Wi-Fi router. The sensor collects water consumption data, which is processed by the ESP8266 and transmitted via Wi-Fi to the web platform. The web platform, developed with HTML, CSS, and JavaScript, receives the data from the prototype, stores it in a database, and presents it to the user in the form of graphs and reports. This platform also allows you to configure consumption limits and define personalized alerts. The project is divided into two crucial phases: Phase 1: Prototype assembly and programming: In this step, the prototype is assembled with the aforementioned components. The ESP8266 microcontroller is programmed to collect data from the sensor, process it, and transmit it via Wi-Fi to the platform. Phase 2: Web platform development: The platform is built to receive, store and present the prototype data. Functionalities such as real-time visualization, graphing and reporting, setting alerts and defining consumption limits can be implemented. The success of the project depends on the seamless integration between the prototype and the web platform, ensuring the collection, processing and presentation of water consumption data in an efficient and reliable manner.