Um Sistema de Informação para Monitoramento e Avaliação da Qualidade dos Grãos em Unidades Armazenadoras Usando Dados de Sensores de IoT e Outros Mecanismos

• Autor(a) principal: Carvalho, Fernando
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações do UNIOESTE
• Texto Completo: https://tede.unioeste.br/handle/tede/6031
• topic: Armazenamento de Grãos; Monitoramento; IoT; Grain Storage; Monitoring; IoT; CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO

According to 2018 data, Brazil is the third largest food exporter in the world, of which a significant part is cereal grains. In the post-harvest phase, these grains are stored in large structures designed to maintain the physicochemical properties of the food, which is one of the most critical phases. This is one of the most critical phases. This preservation is mainly done by controlling the ambient temperature, measuring the temperature of the stored grain and aerating the grain. Field research has shown that this operation, if performed manually, can lead to losses of part of the stocks, mainly caused by human errors in the interpretation of the monitored information and in decision making. Based on the expansion of parameters and the acquisition of data in real time through IoT devices, this work presents the result of a field research conducted with the objective of determining the level of adoption of information technologies in grain warehouses in the state of Paraná, as well as the level of knowledge of industry managers regarding industrial automation. In addition, some important parameters for maintaining the quality of stored products were studied, including some low-cost IoT devices, sensors and processing boards. These devices can enable the design of monitoring systems for grain storage and helped to develop a prototype computer system that can monitor grain and evaluate the quality of stock in storage units. In this way, the developed system can provide useful information about the grain storage environment, such as calculation of grain moisture and risk assessment of storage conditions, when connected to sensors or fed with data that allow performing estimations.