Multi-sensor data fusion for swine house environment evaluation and control

Data fusion from various sensors can obtain more accurate and reliable results than a single monitoring sensor. The data of air quality in the swine breeding environment comes from various sensors; therefore, the data must be fused. In this study, statistical criteria, batch estimation theory, a fuzzy comprehensive evaluation (FCE) method, as well as an improved analytic hierarchy process (IAHP), were combined to provide accurate data for multi-sensor data fusion modeling. To make a fair comparison, four statistical criteria were employed to process abnormal data gross errors. Besides, an improved batch estimation adaptive weighted fusion method was adopted to, respectively, integrate the data from temperature, humidity, and concentrations of ammonia (NH3), carbon dioxide (CO2), and hydrogen sulfide (H2S) of air quality at the data level. The predetermined weights were given using IAHP combined with the knowledge of experts. To decide the fuzzy membership values of each environmental parameter, we presented a method to establish fuzzy membership functions and devised a calculation method for related parameters. The gaussmf type membership function was employed to build a global fusion model that fits well with IAHP-FCE. The IAHP-FCE method was exploited to comprehensively integrate multi-sensor data at the decision level. Experimental results show that the presented method can improve the data accuracy and provide decision results and scientific basis for the precision control of swine breeding environment.