ABSTRACT Increasing population and demand for animal-derived products has raised the need for improved efficiency in managing and controlling animal production. Given this context, the project aimed to develop a device that aids decision-making in animal production. A hardware system was designed for instant measurement of thermal well-being levels, light intensity, and air gas concentration. This hardware integrated DHT11 sensors, an LDR photoresistor, and an MQ-135 sensor. To validate the system, a 30-day experimental study was conducted in an industrial pig farming setting. The collected data was sent to the Thingspeak server using the HTTP protocol. Data management, filtering, and organization were optimized using developed treatment algorithms. The system presented information on air humidity, temperature, ammonia concentration, CO2 levels, luminosity, and enthalpy through interactive images on a dashboard. In the case of a risk situation, the system automatically notified users with an "ALERT" message, facilitating prompt and efficient management response, and minimizing losses. The sensor calibration process yielded a high coefficient of determination (r2 = 0.98). Thus, the developed IoT device represents a viable solution, providing precise environmental conditions to support producers and enhance their efficiency and sustainability.

ABSTRACT Increasing population and demand for animal-derived products has raised the need for improved efficiency in managing and controlling animal production. Given this context, the project aimed to develop a device that aids decision-making in animal production. A hardware system was designed for instant measurement of thermal well-being levels, light intensity, and air gas concentration. This hardware integrated DHT11 sensors, an LDR photoresistor, and an MQ-135 sensor. To validate the system, a 30-day experimental study was conducted in an industrial pig farming setting. The collected data was sent to the Thingspeak server using the HTTP protocol. Data management, filtering, and organization were optimized using developed treatment algorithms. The system presented information on air humidity, temperature, ammonia concentration, CO2 levels, luminosity, and enthalpy through interactive images on a dashboard. In the case of a risk situation, the system automatically notified users with an "ALERT" message, facilitating prompt and efficient management response, and minimizing losses. The sensor calibration process yielded a high coefficient of determination (r2 = 0.98). Thus, the developed IoT device represents a viable solution, providing precise environmental conditions to support producers and enhance their efficiency and sustainability.