Characterization of the Relationship between the Loess Moisture and Image Grayscale Value
This paper presents a model for estimating the moisture of loess from an image grayscale value. A series of well-controlled air-dry tests were performed on saturated Malan loess, and the moisture content of the loess sample during the desiccation process was automatically recorded while the soil ima...
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MDPI AG
2021-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/23/7983 |
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| author | Qingbing Liu Jinge Wang Hongwei Zheng Tie Hu Jie Zheng |
| author_facet | Qingbing Liu Jinge Wang Hongwei Zheng Tie Hu Jie Zheng |
| author_sort | Qingbing Liu |
| collection | DOAJ |
| description | This paper presents a model for estimating the moisture of loess from an image grayscale value. A series of well-controlled air-dry tests were performed on saturated Malan loess, and the moisture content of the loess sample during the desiccation process was automatically recorded while the soil images were continually captured using a photogrammetric device equipped with a CMOS image sensor. By converting the red, green, and blue (RGB) image into a grayscale one, the relationship between the water content and grayscale value, referred to as the water content–gray value characteristic curve (WGCC), was obtained; the impacts of dry density, particle size distribution, and illuminance on WGCC were investigated. It is shown that the grayscale value increases as the water content decreases; based on the rate of increase of grayscale value, the WGCC can be segmented into three stages: slow-rise, rapid-rise, and asymptotically stable stages. The influences that dry density and particle size distribution have on WGCC are dependent on light reflection and transmission, and this dependence is closely related to soil water types and their relative proportion. Besides, the WGCC for a given soil sample is unique if normalized with illuminance. The mechanism behind the three stages of WGCC is discussed in terms of visible light reflection. A mathematical model was proposed to describe WGCC, and the physical meaning of the model parameters was interpreted. The proposed model is validated independently using another six different types of loess samples and is shown to match well the experimental data. The results of this study can provide a reference for the development of non-contact soil moisture monitoring methods as well as relevant sensors and instruments. |
| first_indexed | 2024-03-10T04:45:18Z |
| format | Article |
| id | doaj.art-42a8916907124248b1727f9f78a9a74c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T04:45:18Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-42a8916907124248b1727f9f78a9a74c2023-11-23T03:02:10ZengMDPI AGSensors1424-82202021-11-012123798310.3390/s21237983Characterization of the Relationship between the Loess Moisture and Image Grayscale ValueQingbing Liu0Jinge Wang1Hongwei Zheng2Tie Hu3Jie Zheng4Three Gorges Research Center for Geohazards of the Ministry of Education, China University of Geosciences, Wuhan 430074, ChinaThree Gorges Research Center for Geohazards of the Ministry of Education, China University of Geosciences, Wuhan 430074, ChinaThree Gorges Research Center for Geohazards of the Ministry of Education, China University of Geosciences, Wuhan 430074, ChinaThree Gorges Research Center for Geohazards of the Ministry of Education, China University of Geosciences, Wuhan 430074, ChinaFaculty of Information Engineering, Wuhan University of Engineering Science, Wuhan 430200, ChinaThis paper presents a model for estimating the moisture of loess from an image grayscale value. A series of well-controlled air-dry tests were performed on saturated Malan loess, and the moisture content of the loess sample during the desiccation process was automatically recorded while the soil images were continually captured using a photogrammetric device equipped with a CMOS image sensor. By converting the red, green, and blue (RGB) image into a grayscale one, the relationship between the water content and grayscale value, referred to as the water content–gray value characteristic curve (WGCC), was obtained; the impacts of dry density, particle size distribution, and illuminance on WGCC were investigated. It is shown that the grayscale value increases as the water content decreases; based on the rate of increase of grayscale value, the WGCC can be segmented into three stages: slow-rise, rapid-rise, and asymptotically stable stages. The influences that dry density and particle size distribution have on WGCC are dependent on light reflection and transmission, and this dependence is closely related to soil water types and their relative proportion. Besides, the WGCC for a given soil sample is unique if normalized with illuminance. The mechanism behind the three stages of WGCC is discussed in terms of visible light reflection. A mathematical model was proposed to describe WGCC, and the physical meaning of the model parameters was interpreted. The proposed model is validated independently using another six different types of loess samples and is shown to match well the experimental data. The results of this study can provide a reference for the development of non-contact soil moisture monitoring methods as well as relevant sensors and instruments.https://www.mdpi.com/1424-8220/21/23/7983loesswater contentdigital imagesgrayscale valuemathematical model |
| spellingShingle | Qingbing Liu Jinge Wang Hongwei Zheng Tie Hu Jie Zheng Characterization of the Relationship between the Loess Moisture and Image Grayscale Value Sensors loess water content digital images grayscale value mathematical model |
| title | Characterization of the Relationship between the Loess Moisture and Image Grayscale Value |
| title_full | Characterization of the Relationship between the Loess Moisture and Image Grayscale Value |
| title_fullStr | Characterization of the Relationship between the Loess Moisture and Image Grayscale Value |
| title_full_unstemmed | Characterization of the Relationship between the Loess Moisture and Image Grayscale Value |
| title_short | Characterization of the Relationship between the Loess Moisture and Image Grayscale Value |
| title_sort | characterization of the relationship between the loess moisture and image grayscale value |
| topic | loess water content digital images grayscale value mathematical model |
| url | https://www.mdpi.com/1424-8220/21/23/7983 |
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