论文标题

机器学习和土壤湿度感应:信号强度方法

Machine Learning and Soil Humidity Sensing: Signal Strength Approach

论文作者

Rodić, Lea Dujić, Županović, Tomislav, Perković, Toni, Šolić, Petar, Rodrigues, Joel J. P. C.

论文摘要

无处不在和普遍计算的IoT愿景产生了包括物理和数字世界的未来智能灌溉系统。智能灌溉生态系统与机器学习相结合可以提供成功解决土壤湿度传感任务的解决方案,以确保最佳的用水量。现有解决方案基于从饥饿/昂贵的传感器收到的数据,这些传感器正在通过无线通道传输感应数据。随着时间的流逝,系统变得难以维护,尤其是在偏远地区,由于电池更换了大量设备。因此,新颖的解决方案必须提供一种替代,成本和能源有效的设备,该设备比现有解决方案具有独特的优势。这项工作探讨了一种新型,低功率,基于洛拉的,具有成本效益的系统的概念,该系统可以使用深度学习技术实现湿度感测,该技术可以通过仅通过测量给定地下信标设备的信号强度来使用高精度来感知土壤湿度。

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.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源