Smartphone-based spectroscopy as a tool to estimate soil attributes for the citizen science concept
This study explores the potential of using smartphone cameras to estimate soil attributes as a field-based alternative to traditional sampling and lab analyses that are time-consuming, expensive, and non-friendly to the environment. It aims to investigate whether the color information captured by sm...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375523001569 |
| _version_ | 1797674916574658560 |
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| author | Sharad Kumar Gupta Bar Efrati Or Amir Nicolas Francos Marcelo Sternberg Eyal Ben-Dor |
| author_facet | Sharad Kumar Gupta Bar Efrati Or Amir Nicolas Francos Marcelo Sternberg Eyal Ben-Dor |
| author_sort | Sharad Kumar Gupta |
| collection | DOAJ |
| description | This study explores the potential of using smartphone cameras to estimate soil attributes as a field-based alternative to traditional sampling and lab analyses that are time-consuming, expensive, and non-friendly to the environment. It aims to investigate whether the color information captured by smartphone cameras could provide spectral information that can be utilized for soil attribute estimation using regression techniques. The DN values from smartphone camera photos were transformed into reflectance using colored plastic plated marker (PPM) reference panels. The spectral information of the PPM keys, obtained using a laboratory spectrometer, was convolved into 3 RGB smartphone bands. Using the legacy soil spectral library (SSL) of Israel on the 3-reflectance calibrated RGB smartphone bands, a good agreement with the reflectance of the laboratory spectrometer was obtained. The soil properties were modeled with the converted reflectance using the multiple linear regression (MLR), partial least square regression (PLSR), and ridge regression (RR) algorithms. The models created using the smartphone data (RGB and 6 synthetic bands) predicted six soil properties: i.e., soil organic matter (OM%), CaCO3, free iron oxides (Fe-dithionite), Al2O3, soil surface area (SSA), and hygroscopic moisture with the coefficient of determination (R2) values of 0.58, 0.54, 0.60, 0.55, 0.55 and 0.64, respectively. The quality of predictions was also evaluated using root mean square error (RMSE) and the ratio of prediction to interquartile distance (RPIQ). This research provides the results of an experiment to convert a personal smartphone camera to a spectrometer with the help of SSL. Despite the low spectral resolution of smartphone cameras, the models achieved fair results in predicting six soil properties. These findings could promote the adoption of citizen science methodologies for sustainable soil management as smartphone devices that are available to all can be simply calibrated to reflectance and used for proximal sensing of soils. |
| first_indexed | 2024-03-11T22:07:07Z |
| format | Article |
| id | doaj.art-9f61f49a568f49789261fc90dcdd5d47 |
| institution | Directory Open Access Journal |
| issn | 2772-3755 |
| language | English |
| last_indexed | 2024-03-11T22:07:07Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj.art-9f61f49a568f49789261fc90dcdd5d472023-09-25T04:12:35ZengElsevierSmart Agricultural Technology2772-37552023-10-015100327Smartphone-based spectroscopy as a tool to estimate soil attributes for the citizen science conceptSharad Kumar Gupta0Bar Efrati1Or Amir2Nicolas Francos3Marcelo Sternberg4Eyal Ben-Dor5Remote Sensing Laboratory, School of Environment and Earth Sciences, Tel Aviv University, Tel Aviv, Israel; Plant Ecology Laboratory, School of Plant Science and Food Security, Tel Aviv University, Tel Aviv, Israel; Corresponding author at: Remote Sensing Laboratory, School of Environment and Earth Sciences, Tel Aviv University, Israel.Remote Sensing Laboratory, School of Environment and Earth Sciences, Tel Aviv University, Tel Aviv, IsraelRemote Sensing Laboratory, School of Environment and Earth Sciences, Tel Aviv University, Tel Aviv, IsraelRemote Sensing Laboratory, School of Environment and Earth Sciences, Tel Aviv University, Tel Aviv, IsraelPlant Ecology Laboratory, School of Plant Science and Food Security, Tel Aviv University, Tel Aviv, IsraelRemote Sensing Laboratory, School of Environment and Earth Sciences, Tel Aviv University, Tel Aviv, IsraelThis study explores the potential of using smartphone cameras to estimate soil attributes as a field-based alternative to traditional sampling and lab analyses that are time-consuming, expensive, and non-friendly to the environment. It aims to investigate whether the color information captured by smartphone cameras could provide spectral information that can be utilized for soil attribute estimation using regression techniques. The DN values from smartphone camera photos were transformed into reflectance using colored plastic plated marker (PPM) reference panels. The spectral information of the PPM keys, obtained using a laboratory spectrometer, was convolved into 3 RGB smartphone bands. Using the legacy soil spectral library (SSL) of Israel on the 3-reflectance calibrated RGB smartphone bands, a good agreement with the reflectance of the laboratory spectrometer was obtained. The soil properties were modeled with the converted reflectance using the multiple linear regression (MLR), partial least square regression (PLSR), and ridge regression (RR) algorithms. The models created using the smartphone data (RGB and 6 synthetic bands) predicted six soil properties: i.e., soil organic matter (OM%), CaCO3, free iron oxides (Fe-dithionite), Al2O3, soil surface area (SSA), and hygroscopic moisture with the coefficient of determination (R2) values of 0.58, 0.54, 0.60, 0.55, 0.55 and 0.64, respectively. The quality of predictions was also evaluated using root mean square error (RMSE) and the ratio of prediction to interquartile distance (RPIQ). This research provides the results of an experiment to convert a personal smartphone camera to a spectrometer with the help of SSL. Despite the low spectral resolution of smartphone cameras, the models achieved fair results in predicting six soil properties. These findings could promote the adoption of citizen science methodologies for sustainable soil management as smartphone devices that are available to all can be simply calibrated to reflectance and used for proximal sensing of soils.http://www.sciencedirect.com/science/article/pii/S2772375523001569RGB cameraSmartphoneSoil spectral librarySoil spectroscopySoil healthCitizen science |
| spellingShingle | Sharad Kumar Gupta Bar Efrati Or Amir Nicolas Francos Marcelo Sternberg Eyal Ben-Dor Smartphone-based spectroscopy as a tool to estimate soil attributes for the citizen science concept Smart Agricultural Technology RGB camera Smartphone Soil spectral library Soil spectroscopy Soil health Citizen science |
| title | Smartphone-based spectroscopy as a tool to estimate soil attributes for the citizen science concept |
| title_full | Smartphone-based spectroscopy as a tool to estimate soil attributes for the citizen science concept |
| title_fullStr | Smartphone-based spectroscopy as a tool to estimate soil attributes for the citizen science concept |
| title_full_unstemmed | Smartphone-based spectroscopy as a tool to estimate soil attributes for the citizen science concept |
| title_short | Smartphone-based spectroscopy as a tool to estimate soil attributes for the citizen science concept |
| title_sort | smartphone based spectroscopy as a tool to estimate soil attributes for the citizen science concept |
| topic | RGB camera Smartphone Soil spectral library Soil spectroscopy Soil health Citizen science |
| url | http://www.sciencedirect.com/science/article/pii/S2772375523001569 |
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