Intelligent pointer meter interconnection solution for data collection in farmlands

摘要

Some non-digitized measuring instruments such as pointer meters are utilized by some farmlands by virtue of their high stability and accuracy, and the data collection depending on the human movements requires considerable time and effort and has a low technology level. Therefore, an intelligent pointer meter indication reading and automatic collection method is proposed using machine vision and wireless sensor network (WSN) technology. An intelligent pointer meter indication reading device consisting of an image acquisition module, an intelligent control center, a wireless transmission module, and a power module was developed, which could accomplish intelligent meter indication reading and automatic data uploading. Furthermore, a pointer meter indication reading algorithm for a resource-limited environment is proposed. Firstly, the pointer and the circle axis were recognized by using edge detection and the geometrical characteristics of the pointer meter, and then, the scales and scale values were determined using the statistical characteristics of the circles around the pointer's vertex. Next, by utilizing the density-based spatial clustering of applications with noise (DBSCAN) algorithm, we adjusted the scales and the scale values, taking full advantage of the pointer's characteristic that the scales' rotation angles changed linearly with the scale values, which were classified by an SVM taking in the HOG feature as the input. Finally, the indication was computed in terms of the relative position of the pointer between the adjacent scale values, and the accuracy was 94.2%. The proposed method was evaluated by comparing it with some newly published algorithms. The pointer detection method had stronger robustness and anti-interference capability than the image segmentation method and the skeleton extraction method. The Theta(n) value, indicating the complexity of the scale detection algorithm, was considerably smaller than the Theta(n(2)) value, denoting the modified central mapping method used mostly for scale detection. The energy consumption by each module was measured, and the results showed that the image acquisition module accounted for the largest energy consumption, 87.7%, which was more than eight times as much as the second-largest energy consumption, 10.3% (that of the wireless transmission module); the problem of power supply could be solved by using solar panels.