Detecting soil degradation and restoration through a novel coupled sensor and machine learning framework

通过新型耦合传感器和机器学习框架检测土壤退化和恢复

• 批准号: NE/T012307/1
• 负责人: John Quinton
• 金额: $ 119.1万
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FT012307%2F1
• 关键词: Detecting; soil; degradation; restoration; through

OverviewIn this proposal we outline an ambitious cross-disciplinary project focused on detecting soil degradation and restoration through a novel multi-functional soil sensing platform that combines conventional and newly created sensors and a machine learning framework. Our proposed work directly addresses the Signals in the Soil call to 'advance our understanding of dynamic soil processes that operate at different temporal/spatial scales.' Through the creation of an innovative new approach to capturing and analysing high frequency data from in-situ sensors, this project will predict the rate and direction of soil system functions for sites undergoing degradation or restoration. To do this, we will build and train a new mechanistically-informed machine learning system to turn high frequency data on multiple soil functions, such as water infiltration, CO2 production, and surface soil movement, into predictions of longer term changes in soil health including the status of microbial processes, soil organic matter (SOM) content, and other properties and processes. Such an approach could be transformative: a system that will allow short-term sensor data to be used to evaluate longer term soil transformations in key ecosystem functions. We will start our work with a suite of off-the-shelf sensors observing multiple soil functions that can be installed quickly. These data will allow us to rapidly initiate development and training of a novel mechanistically informed machine learning framework. In parallel we will develop two new soil health sensors focused on in-situ real time measurement of decomposition rates and transformation of soil colour that reflects the accumulation or loss of SOM. We will then link these new sensors with a suite of conventional sensors in a novel data collection and networking system coupled to the Swarm satellite network to create a low cost sensor array that can be deployed in remote areas and used to support studies of soil degradation or progress toward restoration worldwide.

概述在本提案中，我们概述了一个雄心勃勃的跨学科项目，重点是通过一个新颖的多功能土壤传感平台来检测土壤退化和恢复，该平台结合了传统和新创建的传感器以及机器学习框架。我们提出的工作直接解决了土壤中的信号问题，以“促进我们对在不同时空尺度上运行的动态土壤过程的理解”。“通过创建一种创新的新方法来捕获和分析来自原位传感器的高频数据，该项目将预测正在退化或恢复的场地的土壤系统功能的速率和方向。为此，我们将构建和训练一个新的机械信息机器学习系统，将多个土壤功能的高频数据（如水分渗透，CO2产生和表层土壤运动）转化为土壤健康长期变化的预测，包括微生物过程的状态，土壤有机质（SOM）含量以及其他属性和过程。这种方法可能是变革性的：一个系统，将允许短期传感器数据用于评估关键生态系统功能的长期土壤变化。我们将从一套现成的传感器开始工作，这些传感器可以观察多种土壤功能，并且可以快速安装。这些数据将使我们能够快速启动新的机械信息机器学习框架的开发和训练。同时，我们将开发两种新的土壤健康传感器，专注于现场真实的时间测量的分解率和土壤颜色的转换，反映了积累或损失的SOM。然后，我们将把这些新的传感器与一套传统的传感器连接在一个新的数据收集和网络系统中，该系统与Swarm卫星网络相连，以创建一个低成本的传感器阵列，可以部署在偏远地区，用于支持全球土壤退化或恢复进展的研究。

项目成果

Characterising the ice sheet surface in Northeast Greenland using Sentinel-1 SAR data

• DOI: 10.1017/jog.2023.64
• 发表时间: 2023-08
• 期刊: Journal of Glaciology
• 影响因子: 3.4
• 作者: Qingying Shu;Rebecca Killick;A. Leeson;C. Nemeth;X. Fettweis;A. Hogg;David Leslie

A Transient Printed Soil Decomposition Sensor Based on a Biopolymer Composite Conductor.

• DOI: 10.1002/advs.202205785
• 发表时间: 2023-02
• 期刊: ADVANCED SCIENCE
• 影响因子: 15.1
• 作者: Atreya, Madhur;Desousa, Stacie;Kauzya, John-Baptist;Williams, Evan;Hayes, Austin;Dikshit, Karan;Nielson, Jenna;Palmgren, Abigail;Khorchidian, Sara;Liu, Shangshi;Gopalakrishnan, Anupam;Bihar, Eloise;Bruns, Carson J. J.;Bardgett, Richard;Quinton, John N. N.;Davies, Jessica;Neff, Jason C. C.;Whiting, Gregory L. L.
• 通讯作者: Whiting, Gregory L. L.

Wax Blends as Tunable Encapsulants for Soil-Degradable Electronics

• DOI: 10.1021/acsaelm.2c00833
• 发表时间: 2022-10-10
• 期刊: ACS APPLIED ELECTRONIC MATERIALS
• 影响因子: 4.7
• 作者: Atreya, Madhur;Marinick, Gabrielle;Whiting, Gregory L.
• 通讯作者: Whiting, Gregory L.