基于机器学习的土壤VWC和低成本传感器田间持水量估算
Machine Learning based Soil VWC and Field Capacity Estimation Using Low Cost Sensors
摘要：
土壤中存在的水量是根据通常称为体积含水量（VWC）的参数来测量的，并用于确定任何土壤的田间持水量。这是一个重要的参数，说明如何确保在种植园进行适当的灌溉，以进行耕作和造林活动。目前的工作是一个扩展，已经在该地区的无线地下传感器网络（WUSNs）的研究。传感器节点配备Decagon 5TM体积含水量（VWC）和温度传感器部署在地下，以了解农业活动的土壤属性。在大范围内部署这种网络的主要障碍是VWC传感器的成本和这些传感器收集的数据的可信度。在本文中，我们分析了使用低成本的湿度和温度传感器，可以用来估计VWC值和现场容量或交叉验证昂贵的VWC传感器的数据之前，实际部署。机器学习算法，即神经网络和随机森林的研究预测VWC值从低成本的水分传感器。几个现场实验进行了检验所提出的假设。结果表明，低成本的水分传感器可以帮助估计的VWC和田间容量与一个小的权衡。
英文摘要：
The amount of water present in soil is measured in terms of a parameter commonly referred to as Volumetric Water Content (VWC) and is used for determining the field capacity of any soil. It is an important parameter accounting for ensuring proper irrigation at plantation sites for farming as well as for afforestation activities. The current work is an extension to already going on research in the area of wireless underground sensor networks (WUSNs). Sensor nodes equipped with Decagon 5TM volumetric water content (VWC) and temperature sensor are deployed underground to understand the properties of soil for agricultural activities. The major hindrances in the deployment of such networks over a large field are the cost of VWC sensors and the credibility of the data being collected by these sensors. In this paper, we analyze the use of low-cost moisture and temperature sensors that can either be used to estimate the VWC values and field capacity or cross-validate the data of expensive VWC sensors before the actual deployment. Machine learning algorithms, namely Neural Networks and Random Forests are investigated for predicting the VWC value from low-cost moisture sensors. Several field experiments are carried out to examine the proposed hypothesis. The results showed that low-cost moisture sensors can assist in estimating the VWC and field capacity with a minor trade-off.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
