Abstract
This paper presents a web-based data acquisition system developed by LabVIEW software program using for environment monitoring of poultry management. Measurement error and uncertainty analysis should be conducted accurately to maximize the reliability of this system. An algorithm for uncertainty analysis was proposed to estimate the sensor networks with calibration and validation processes fulfilling the standards of Guide to the Expression of Uncertainty in Measurement (GUM). For different environmental sensors, values of uncertainties were calculated with the methods of type A and type B evaluation through a case study. Environmental parameters including air temperature, relative humidity and CO2 concentration of a compartment housing for a small-group (n=90) of laying hens with a perch aviary system were measured in a 24-h period with an interval of 5 min. Results showed that the expanded uncertainty of the data acquisition system lies above 1.02℃, 5.54% and 67.8 ppm over the sensors data from air temperature, relative humidity and CO2 concentration with perch system for laying hens. Moreover, the relative uncertainty of this system was estimated by 15.9%. Therefore, the web-based data acquisition system has a considerable potential to ensure correct decision making when served for poultry production based on more reliable uncertaity analysis and data evaluation.
Highlights
In recent years, the improvement of informative networks and sensor technologies has enabled the rapid growth of wireless and online management systems
The specific objectives of this work were: (1) to introduce a web-based data acquisition system using the LabVIEW software program to measure environmental parameters for poultry management, (2) to deliver an algorithm for the uncertainty analysis of various sensor data based on the methods of type A and B evaluation, and (3) to examine the system performances based on the uncertainty analysis of the sensor network
In this data acquisition system, the stability of the air temperature is calculated as 0.49 °C, mean measurement error of the relative humidity is limited to 0.72% by Equation (4), and measurement uncertainty of CO2 concentration for the methods of type A and B evaluation multiplied by the coverage factor (k = 2) is 67.8 ppm
Summary
The improvement of informative networks and sensor technologies has enabled the rapid growth of wireless and online management systems. These systems with sensor networks can remotely monitor and maintain communication with many unfavorable physical environments such as remote geographic regions, inaccessible dangerous locations, and commercial poultry farms (Georgiadis et al, 2009; So-In et al, 2014; Venkatraman et al, 2016; Zahedi et al, 2016). It is important to evaluate the measurement uncertainty and sensor network errors with a web-based data acquisition and remote management system so that the uncertainty of the system can be estimated correctly (Kessel et al, 2008; Pechlivanidis et al, 2011; Sarachi et al, 2015). Analyzing the sensor data will increase the confidence of the authorities in the results to make decisions
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