1 research outputs found
Machine Learning and Soil Humidity Sensing: Signal Strength Approach
The IoT vision of ubiquitous and pervasive computing gives rise to future
smart irrigation systems comprising physical and digital world. Smart
irrigation ecosystem combined with Machine Learning can provide solutions that
successfully solve the soil humidity sensing task in order to ensure optimal
water usage. Existing solutions are based on data received from the power
hungry/expensive sensors that are transmitting the sensed data over the
wireless channel. Over time, the systems become difficult to maintain,
especially in remote areas due to the battery replacement issues with large
number of devices. Therefore, a novel solution must provide an alternative,
cost and energy effective device that has unique advantage over the existing
solutions. This work explores a concept of a novel, low-power, LoRa-based,
cost-effective system which achieves humidity sensing using Deep learning
techniques that can be employed to sense soil humidity with the high accuracy
simply by measuring signal strength of the given underground beacon device