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Active Learning for Efficient Soil Monitoring in Large Terrain with Heterogeneous Sensor Network

Soils are a complex ecosystem that provides critical services, such as growing food, supplying antibiotics, filtering wastes, and maintaining biodiversity; hence monitoring soil health and domestication is required for sustainable human development. Low-cost and high-resolution soil monitoring syste...

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Detalles Bibliográficos
Autores principales: Chen, Hui, Wang, Ju
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007343/
https://www.ncbi.nlm.nih.gov/pubmed/36904569
http://dx.doi.org/10.3390/s23052365
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author Chen, Hui
Wang, Ju
author_facet Chen, Hui
Wang, Ju
author_sort Chen, Hui
collection PubMed
description Soils are a complex ecosystem that provides critical services, such as growing food, supplying antibiotics, filtering wastes, and maintaining biodiversity; hence monitoring soil health and domestication is required for sustainable human development. Low-cost and high-resolution soil monitoring systems are challenging to design and build. Compounded by the sheer size of the monitoring area of interest and the variety of biological, chemical, and physical parameters to monitor, naive approaches to adding or scheduling more sensors will suffer from cost and scalability problems. We investigate a multi-robot sensing system integrated with an active learning-based predictive modeling technique. Taking advantage of advances in machine learning, the predictive model allows us to interpolate and predict soil attributes of interest from the data collected by sensors and soil surveys. The system provides high-resolution prediction when the modeling output is calibrated with static land-based sensors. The active learning modeling technique allows our system to be adaptive in data collection strategy for time-varying data fields, utilizing aerial and land robots for new sensor data. We evaluated our approach using numerical experiments with a soil dataset focusing on heavy metal concentration in a flooded area. The experimental results demonstrate that our algorithms can reduce sensor deployment costs via optimized sensing locations and paths while providing high-fidelity data prediction and interpolation. More importantly, the results verify the adapting behavior of the system to the spatial and temporal variations of soil conditions.
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spelling pubmed-100073432023-03-12 Active Learning for Efficient Soil Monitoring in Large Terrain with Heterogeneous Sensor Network Chen, Hui Wang, Ju Sensors (Basel) Article Soils are a complex ecosystem that provides critical services, such as growing food, supplying antibiotics, filtering wastes, and maintaining biodiversity; hence monitoring soil health and domestication is required for sustainable human development. Low-cost and high-resolution soil monitoring systems are challenging to design and build. Compounded by the sheer size of the monitoring area of interest and the variety of biological, chemical, and physical parameters to monitor, naive approaches to adding or scheduling more sensors will suffer from cost and scalability problems. We investigate a multi-robot sensing system integrated with an active learning-based predictive modeling technique. Taking advantage of advances in machine learning, the predictive model allows us to interpolate and predict soil attributes of interest from the data collected by sensors and soil surveys. The system provides high-resolution prediction when the modeling output is calibrated with static land-based sensors. The active learning modeling technique allows our system to be adaptive in data collection strategy for time-varying data fields, utilizing aerial and land robots for new sensor data. We evaluated our approach using numerical experiments with a soil dataset focusing on heavy metal concentration in a flooded area. The experimental results demonstrate that our algorithms can reduce sensor deployment costs via optimized sensing locations and paths while providing high-fidelity data prediction and interpolation. More importantly, the results verify the adapting behavior of the system to the spatial and temporal variations of soil conditions. MDPI 2023-02-21 /pmc/articles/PMC10007343/ /pubmed/36904569 http://dx.doi.org/10.3390/s23052365 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Hui
Wang, Ju
Active Learning for Efficient Soil Monitoring in Large Terrain with Heterogeneous Sensor Network
title Active Learning for Efficient Soil Monitoring in Large Terrain with Heterogeneous Sensor Network
title_full Active Learning for Efficient Soil Monitoring in Large Terrain with Heterogeneous Sensor Network
title_fullStr Active Learning for Efficient Soil Monitoring in Large Terrain with Heterogeneous Sensor Network
title_full_unstemmed Active Learning for Efficient Soil Monitoring in Large Terrain with Heterogeneous Sensor Network
title_short Active Learning for Efficient Soil Monitoring in Large Terrain with Heterogeneous Sensor Network
title_sort active learning for efficient soil monitoring in large terrain with heterogeneous sensor network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007343/
https://www.ncbi.nlm.nih.gov/pubmed/36904569
http://dx.doi.org/10.3390/s23052365
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