Combining multiple proximal sensors within a wireless sensor network (WSN)\r\nenhances our capacity to monitor vegetation, compared to using a single sensor or\r\nnon-networked setup. Data from sensors with different spatial and temporal characteristics\r\ncan provide complementary information. For example, point-based sensors such as\r\nmultispectral sensors which monitor at high temporal frequency but, at a single point, can\r\nbe complemented by array-based sensors such as digital cameras which have greater spatial\r\nresolution but may only gather data at infrequent intervals. In this article we describe the\r\nsuccessful deployment of a prototype system for using multiple proximal sensors\r\n(multispectral sensors and digital cameras) for monitoring pastures. We show that there are\r\nmany technical issues involved in such a deployment, and we share insights relevant for\r\nother researchers who may consider using WSNs for an operational deployment for pasture\r\nmonitoring under often difficult environmental conditions. Although the sensors and infrastructure are important, we found that other issues arise and that an end-to-end\r\nworkflow is an essential part of effectively capturing, processing and managing the data\r\nfrom a WSN. Our deployment highlights the importance of testing and ongoing monitoring\r\nof the entire workflow to ensure the quality of data captured. We demonstrate that the\r\ncombination of different sensors enhances our ability to identify sensor problems necessary\r\nto collect accurate data for pasture monitoring.
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