Departamento de Ensino Superior
URI permanente desta comunidadehttps://ri.ifam.edu.br/handle/4321/956
Navegar
2 resultados
Resultados da Pesquisa
Trabalho de Conclusão de Curso Métodos para reconhecimento de resíduos recicláveis através de visão computacional(2025-02-10) Silva, Vitor Arlinson Rodrigues da; Pereira, Micila Sumária Medeiros; http://lattes.cnpq.br/7877913821937987; Santos, Alyson de Jesus dos; http://lattes.cnpq.br/5998752909180697; Fialho, Edevaldo Albuquerque; http://lattes.cnpq.br/1681351413618686; Santos, Renan Cavalcante; http://lattes.cnpq.br/6930748017205035This work proposes the study of methods that seek to unite concepts from society 5.0 and industry 4.0 to be used in favor of sustainability. With the increase in the production of plastic waste around the world and its low recycling rate, there was a need to reduce the impact on the ecosystem. The methodology used was applied, explanatory research with a technological design. As a result, three methods were developed for recognizing recyclable waste. Finally, the method selected used a convolutional neural network to create and train a classifier for a machine, powered by plastic and metal lids. The aim of this system is to promote sustainability and reduce the amount of recyclable materials disposed of inappropriately in the environment. Therefore, this work aims to develop low-cost sorters and select the best one to be used in an machine powered by recyclable waste.Trabalho de Conclusão de Curso 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/6704112028750834This 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.