Development and Application of a Vehicle-Mounted Soil Texture Detector
Abstract
:1. Introduction
2. Materials and Methods
2.1. Soil Texture Prediction Method
2.2. Soil EC Measurement Principle
- —Calculated soil EC (μS/cm)
- , , , —The distance between the probes (cm)
- —Constant current source current (A)
- —Voltage between M and N probes (V)
2.3. Principles of Soil Surface Image Analysis
- P—Image
- (i,j)—Pixel coordinates
- P—Image
- (i,j)—Pixel coordinates
- P—Image
- (i,j)—Pixel coordinates
2.4. Detector Design
2.5. Soil Samples and Experimental Preparation
3. Results
3.1. Electrical Conductivity and Texture Features
3.2. Analysis of Soil EC Measurement Results
3.3. Analysis of Soil Texture Measurement Results
4. Discussion
5. Conclusions
- Based on the feasibility analysis of the texture measurement principle, the soil EC is combined with the machine vision device, and the SVM model is used as the embedded model to obtain and analyze the in-situ texture information of farmland soil in real time. Compare the results obtained by this method with the standard method data. The results show that this method of obtaining in-situ texture information of farmland soil does not require chemical reagents, long test time, and artificial energy, and it is a new method to quickly obtain soil texture in real time.
- The correlation analysis between the farmland measurement results of the in-situ vehicle-mounted soil texture detector and the results obtained by the laboratory standard method is carried out, and the correlation analysis result R2 of the soil EC measurement is 0.75. The accuracy rate of soil texture prediction data obtained by combining EC and GLCM using the embedded model reached 84.86%. The results show that the vehicle-mounted soil texture detector combined with EC and GLCM can predict the soil texture of the target plot based on the original information of the farmland, and has high accuracy.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Soil Texture | Physical Clay (<0.01 mm) Content | Physical Clay (>0.01 mm) Content | |||||
---|---|---|---|---|---|---|---|
Podzol | Grassland Soil, Red and Yellow Soil | Columnar Alkaline Soil, Strong Alkaline Soil | Podzol | Grassland Soil, Red and Yellow Soil | Columnar Alkaline Soil, Strong Alkaline Soil | ||
Sand | Loose sand | 0–5 | 0–5 | 0–5 | 100–95 | 100–95 | 100–90 |
Tight sand | 5–10 | 5–10 | 5–10 | 95–90 | 95–90 | 95–90 | |
Loam | Sandy loam | 10–20 | 10–20 | 10–15 | 90–80 | 90–80 | 90–85 |
Light loam | 20–30 | 20–30 | 15–20 | 80–70 | 80–70 | 85–80 | |
Middle loam | 30–40 | 30–45 | 20–30 | 70–60 | 70–55 | 80–70 | |
Heavy loam | 40–50 | 45–60 | 30–40 | 60–50 | 55–40 | 70–60 | |
Clay | Light clay | 50–65 | 60–75 | 40–50 | 50–30 | 40–25 | 60–50 |
Medium clay | 65–80 | 75–85 | 50–65 | 35–20 | 25–15 | 50–35 | |
Heavy clay | >80 | >85 | >65 | <20 | <15 | <35 |
Texture | GLCM and EC | Mean | Standard Error | Median | Standard Deviation | Kurtosis | Skewness |
---|---|---|---|---|---|---|---|
Sandy loam | Energy | 0.0221 | 0.0021 | 0.0190 | 0.0159 | 5.7348 | 2.3370 |
Entropy | 4.4408 | 0.0602 | 4.5656 | 0.4662 | 2.6134 | −1.6601 | |
M of I | 5.0549 | 0.3172 | 5.8599 | 2.4570 | −0.8623 | −0.4730 | |
Correlation | 0.0609 | 0.0039 | 0.0522 | 0.0301 | 28.9858 | 4.9598 | |
EC | 271.665 | 8.9177 | 257.000 | 69.0765 | 0.3877 | 0.9216 | |
Light loam | Energy | 0.0360 | 0.0021 | 0.0326 | 0.0202 | 17.2707 | 2.7564 |
Entropy | 3.7861 | 0.0490 | 3.6955 | 0.4800 | −0.0321 | 0.6633 | |
M of I | 1.6319 | 0.2023 | 0.7209 | 1.9823 | 2.8862 | 2.0293 | |
Correlation | 0.1091 | 0.0067 | 0.0966 | 0.0652 | 20.0478 | 3.0386 | |
EC | 225.0635 | 6.3721 | 201.500 | 62.4339 | 1.5693 | 1.2774 | |
Middle loam | Energy | 0.0431 | 0.0029 | 0.0376 | 0.0145 | −0.1002 | 0.7838 |
Entropy | 3.4965 | 0.0556 | 3.5691 | 0.2779 | −1.1965 | −0.2736 | |
M of I | 0.5814 | 0.0411 | 0.5107 | 0.2053 | −1.4442 | 0.3729 | |
Correlation | 0.1219 | 0.0096 | 0.1046 | 0.0479 | 1.5727 | 1.3814 | |
EC | 218.784 | 11.676 | 199.300 | 58.3801 | 1.3856 | 1.2557 |
Input | Predicted Soil Texture | |||||
---|---|---|---|---|---|---|
Sandy Loam | Light Loam | Middle Loam | Correct Rate | |||
Actual soil texture | Sandy loam | 32 | 31 | 0 | 50.79% | |
EC | Light loam | 25 | 72 | 0 | 74.23% | |
Middle loam | 4 | 17 | 4 | 16% | ||
Total correct rate | - | - | - | - | 56.21% | |
Actual soil texture | Sandy loam | 52 | 10 | 1 | 82.59% | |
GLCM | Light loam | 11 | 85 | 1 | 87.63% | |
Middle loam | 0 | 15 | 10 | 40% | ||
Total correct rate | - | - | - | - | 78.38% | |
Actual soil texture | Sandy loam | 56 | 7 | 0 | 88.89% | |
EC and GLCM | Light loam | 8 | 85 | 4 | 87.63% | |
Middle loam | 0 | 9 | 16 | 64% | ||
Total correct rate | - | - | - | - | 84.86% |
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Meng, C.; Yang, W.; Lan, H.; Ren, X.; Li, M. Development and Application of a Vehicle-Mounted Soil Texture Detector. Sensors 2020, 20, 7175. https://doi.org/10.3390/s20247175
Meng C, Yang W, Lan H, Ren X, Li M. Development and Application of a Vehicle-Mounted Soil Texture Detector. Sensors. 2020; 20(24):7175. https://doi.org/10.3390/s20247175
Chicago/Turabian StyleMeng, Chao, Wei Yang, Hong Lan, Xinjian Ren, and Minzan Li. 2020. "Development and Application of a Vehicle-Mounted Soil Texture Detector" Sensors 20, no. 24: 7175. https://doi.org/10.3390/s20247175
APA StyleMeng, C., Yang, W., Lan, H., Ren, X., & Li, M. (2020). Development and Application of a Vehicle-Mounted Soil Texture Detector. Sensors, 20(24), 7175. https://doi.org/10.3390/s20247175