迈向低成本rssi作物监测

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Internet of Things Pub Date : 2020-06-19 DOI:10.1145/3393667
Jan Bauer, N. Aschenbruck
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引用次数: 9

摘要

在精准农业的背景下,持续监测作物生长对特定地点和可持续的农场管理至关重要。在精确的现场信息的帮助下,农业实践,如灌溉、施肥和植物保护,可以动态地适应个别地点不断变化的需求,从而支持产量增加和资源优化。如今,部署在温室和农田中的联网传感器的物联网技术已经为现场信息做出了贡献。除了现有的用于水分或养分监测的土壤传感器外,还有(主要是光学)传感器用于评估作物的生长发育和生命条件。本文提出了一种基于低功耗物联网无线电通信信号强度时间变化的低成本作物传感的新颖互补方法。为此,研究了以叶面积指数(LAI)为代表的作物生长与低成本无线电收发器信号传播衰减的关系。小麦田间实测表明,LAI与接收信号强度指标(RSSI)时间序列之间存在显著的相关关系。并对影响气象因子进行了识别和分析。考虑到这些因素,最终建立了一个多元线性模型,使基于rssi的LAI估计具有很大的潜力。
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Towards a Low-cost RSSI-based Crop Monitoring
The continuous monitoring of crop growth is crucial for site-specific and sustainable farm management in the context of precision agriculture. With the help of precise in situ information, agricultural practices, such as irrigation, fertilization, and plant protection, can be dynamically adapted to the changing needs of individual sites, thereby supporting yield increases and resource optimization. Nowadays, IoT technology with networked sensors deployed in greenhouses and farmlands already contributes to in situ information. In addition to existing soil sensors for moisture or nutrient monitoring, there are also (mainly optical) sensors to assess growth developments and vital conditions of crops. This article presents a novel and complementary approach for a low-cost crop sensing that is based on temporal variations of the signal strength of low-power IoT radio communication. To this end, the relationship between crop growth, represented by the leaf area index (LAI), and the attenuation of signal propagation of low-cost radio transceivers is investigated. Real-world experiments in wheat fields show a significant correlation between LAI and received signal strength indicator (RSSI) time series. Moreover, influencing meteorological factors are identified and their effects are analyzed. Including these factors, a multiple linear model is finally developed that enables an RSSI-based LAI estimation with great potential.
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来源期刊
CiteScore
5.20
自引率
3.70%
发文量
0
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