Development of a Low-Cost Sensor System for Accurate Soil Assessment and Biological Activity Profiling
The development of low-cost tools for rapid soil assessment has become a crucial field due to the increasing demands in food production and carbon storage. However, current methods for soil evaluation are costly and cannot provide enough information about the quality of samples. This work reports fo...
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MDPI AG
2024-10-01
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| Online Access: | https://www.mdpi.com/2072-666X/15/11/1293 |
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| author | Antonio Ruiz-Gonzalez Harriet Kempson Jim Haseloff |
| author_facet | Antonio Ruiz-Gonzalez Harriet Kempson Jim Haseloff |
| author_sort | Antonio Ruiz-Gonzalez |
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| description | The development of low-cost tools for rapid soil assessment has become a crucial field due to the increasing demands in food production and carbon storage. However, current methods for soil evaluation are costly and cannot provide enough information about the quality of samples. This work reports for the first time a low-cost 3D printed device that can be used for soil classification as well as the study of biological activity. The system incorporated multiple physical and gas sensors for the characterisation of sample types and profiling of soil volatilome. Sensing data were obtained from 31 variables, including 18 individual light wavelengths that could be used to determine seed germination rates of tomato plants. A machine learning algorithm was trained using the data obtained by characterising 75 different soil samples. The algorithm could predict seed germination rates with high accuracy (RSMLE = 0.01, and R<sup>2</sup> = 0.99), enabling an objective and non-invasive study of the impact of multiple environmental parameters in soil quality. To allow for a more complete profiling of soil biological activity, molecular imprinted-based fine particles were designed to quantify tryptophol, a quorum-sensing signalling molecule commonly used by fungal populations. This device could quantify the concentration of tryptophol down to 10 nM, offering the possibility of studying the interactions between fungi and bacterial populations. The final device could monitor the growth of microbial populations in soil, and offering an accurate assessment of quality at a low cost, impacting germination rates by incorporating hybrid data from the microsensors. |
| format | Article |
| id | doaj-art-9697c052fd114cc9addda3a94efcd512 |
| institution | OA Journals |
| issn | 2072-666X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Micromachines |
| spelling | doaj-art-9697c052fd114cc9addda3a94efcd5122025-08-20T02:05:06ZengMDPI AGMicromachines2072-666X2024-10-011511129310.3390/mi15111293Development of a Low-Cost Sensor System for Accurate Soil Assessment and Biological Activity ProfilingAntonio Ruiz-Gonzalez0Harriet Kempson1Jim Haseloff2Department of Plant Sciences, University of Cambridge, Downing St., Cambridge CB2 3EA, UKDepartment of Plant Sciences, University of Cambridge, Downing St., Cambridge CB2 3EA, UKDepartment of Plant Sciences, University of Cambridge, Downing St., Cambridge CB2 3EA, UKThe development of low-cost tools for rapid soil assessment has become a crucial field due to the increasing demands in food production and carbon storage. However, current methods for soil evaluation are costly and cannot provide enough information about the quality of samples. This work reports for the first time a low-cost 3D printed device that can be used for soil classification as well as the study of biological activity. The system incorporated multiple physical and gas sensors for the characterisation of sample types and profiling of soil volatilome. Sensing data were obtained from 31 variables, including 18 individual light wavelengths that could be used to determine seed germination rates of tomato plants. A machine learning algorithm was trained using the data obtained by characterising 75 different soil samples. The algorithm could predict seed germination rates with high accuracy (RSMLE = 0.01, and R<sup>2</sup> = 0.99), enabling an objective and non-invasive study of the impact of multiple environmental parameters in soil quality. To allow for a more complete profiling of soil biological activity, molecular imprinted-based fine particles were designed to quantify tryptophol, a quorum-sensing signalling molecule commonly used by fungal populations. This device could quantify the concentration of tryptophol down to 10 nM, offering the possibility of studying the interactions between fungi and bacterial populations. The final device could monitor the growth of microbial populations in soil, and offering an accurate assessment of quality at a low cost, impacting germination rates by incorporating hybrid data from the microsensors.https://www.mdpi.com/2072-666X/15/11/1293microsensorquorum sensore-noseartificial neural network |
| spellingShingle | Antonio Ruiz-Gonzalez Harriet Kempson Jim Haseloff Development of a Low-Cost Sensor System for Accurate Soil Assessment and Biological Activity Profiling Micromachines microsensor quorum sensor e-nose artificial neural network |
| title | Development of a Low-Cost Sensor System for Accurate Soil Assessment and Biological Activity Profiling |
| title_full | Development of a Low-Cost Sensor System for Accurate Soil Assessment and Biological Activity Profiling |
| title_fullStr | Development of a Low-Cost Sensor System for Accurate Soil Assessment and Biological Activity Profiling |
| title_full_unstemmed | Development of a Low-Cost Sensor System for Accurate Soil Assessment and Biological Activity Profiling |
| title_short | Development of a Low-Cost Sensor System for Accurate Soil Assessment and Biological Activity Profiling |
| title_sort | development of a low cost sensor system for accurate soil assessment and biological activity profiling |
| topic | microsensor quorum sensor e-nose artificial neural network |
| url | https://www.mdpi.com/2072-666X/15/11/1293 |
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