Enhancing the Accuracy of the Low-Cost Capacitive Soil Moisture Sensor SEN0193 for Internet of Things Applications

Abstract Monitoring soil moisture is an essential aspect of comprehending the functioning of terrestrial ecosystems. However, the cost of commercial sensors often challenges their acquisition. In response to this issue, manufacturers have developed several low-cost soil moisture sensors in recent decades, but there is still a need to improve the evaluation of their precision and accuracy. This article assesses the precision and accuracy of a low-cost soil moisture sensor SEN0193. The assessment results highlight the consistent precision of SEN0193 and emphasize the homogeneity of responses among sensors of the same model. The study also underscores the significant impact of soil granulometry on sensor precision, emphasizing the importance of realizing site-specific calibrations. In terms of accuracy, the study reveals that the calibration recommended by the manufacturer tends to overestimate moisture, particularly in soils with smaller granulometry. The study suggests that soil-specific calibration methods utilizing linear equations are more reliable and effective than polynomial and exponential approaches, especially when employing three to five calibration points. This study offers an essential contribution to the advancement of accessible methods for soil moisture monitoring. These methods are crucial for efficiently monitoring soil moisture in agriculture and natural ecosystems.

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Publication Details

Journal
Brazilian Archives of Biology and Technology
Published
2025-01-01
DOI
https://doi.org/10.1590/1678-4324-2025240380
Citations
3
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
2.40

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article

Enhancing the Accuracy of the Low-Cost Capacitive Soil Moisture Sensor SEN0193 for Internet of Things Applications

Anderson da Rocha Gripp, Rodrigo Lemes Martins, Jorge Gabriel Fernandes Genovez, Francisco de Assis Esteves et al.
3 citations
Brazilian Archives of Biology and Technology
Soil Moisture and Remote Sensing
2.40
article

Enhancing the Accuracy of the Low-Cost Capacitive Soil Moisture Sensor SEN0193 for Internet of Things Applications

Anderson da Rocha Gripp, Rodrigo Lemes Martins, Jorge Gabriel Fernandes Genovez, Francisco de Assis Esteves, Rodrigo Weber Felix, Ana Clara de Lemos Pinto Duque
article en
3 citations

Abstract

Abstract Monitoring soil moisture is an essential aspect of comprehending the functioning of terrestrial ecosystems. However, the cost of commercial sensors often challenges their acquisition. In response to this issue, manufacturers have developed several low-cost soil moisture sensors in recent decades, but there is still a need to improve the evaluation of their precision and accuracy. This article assesses the precision and accuracy of a low-cost soil moisture sensor SEN0193. The assessment results highlight the consistent precision of SEN0193 and emphasize the homogeneity of responses among sensors of the same model. The study also underscores the significant impact of soil granulometry on sensor precision, emphasizing the importance of realizing site-specific calibrations. In terms of accuracy, the study reveals that the calibration recommended by the manufacturer tends to overestimate moisture, particularly in soils with smaller granulometry. The study suggests that soil-specific calibration methods utilizing linear equations are more reliable and effective than polynomial and exponential approaches, especially when employing three to five calibration points. This study offers an essential contribution to the advancement of accessible methods for soil moisture monitoring. These methods are crucial for efficiently monitoring soil moisture in agriculture and natural ecosystems.

Brazilian Archives of Biology and TechnologyVol. 68
Universidade Federal do Rio de Janeiro (BR)
Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro
Openalex Percentile: Top 13%
Soil Moisture and Remote Sensing
2.40
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