# Survey for Soil Sensing with IOT and Traditional Systems
## 2 Tradicitonal Soil Sensing System
### Total Carbon and Total Organic Carbon  
An accurate evaluation of carbon content is crucial for understanding soil fertility and nutrient management. Two primary categories are studied in this domain: Total Carbon and Total Organic Carbon. TC encompasses all carbon found in every particle and compound, which includes both TOC and Total Inorganic Carbon (TIC). TC represents the entirety of carbon found in any compound or particle. Total Inorganic Carbon and Total Organic Carbon represent carbon originating from all organic sources that are covalently bound [39]. In the context of soils and sediments, the organic fraction comprises residues from animals, plants, or microorganisms at various decomposition stages, as well as elemental C like coal, charcoal, and graphite [40]. In scenarios where no inorganic carbon forms are present in soils and sediments, the quantity of TC matches the TOC value [41].  
\[Total\ Carbon=Total\ Organic\ Carbon+Total\ Inorganic\ Carbon \tag{3}\]  
The study by [42] explored the potential of diffuse reflectance spectroscopy in predicting TC in Hawaiian agricultural soils. This was accomplished by integrating visible, NIR, and MIR spectral libraries and constructing chemometric models using partial least squares regression (PLSR) and random forest (RF) ensemble tree regression. The models achieved \(R^{2}\) values of 0.95 (VNIR) and 0.94 (MIR) with RMSE values of 2.80% and 3.08%, respectively.  
In a different study, Ref. [43] utilized MIR to assess organic carbon in soils using portable instruments that spanned visible-to-near-infrared and mid-infrared ranges. Their experiments, both on-site and in the laboratory, confirmed the flexibility and potential of handheld MIR instruments over stationary counterparts. The MIR models, developed from finely ground samples, achieved an impressive \(R^{2}\) of 0.86 and an RMSE of 0.11% during cross-validation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Spectral Range** & **Wavelength (nm)** & **Wave Number (cm\({}^{-1}\))** \\ \hline Visible & 380–800 & 26,315–12,500 \\ \hline NIR & 800–2500 & 12,500–4000 \\ \hline MIR & 2500–25,000 & 4000–400 \\ \hline VIS-NIR & 380–2400 & 26,315–4167 \\ \hline VIS-NIR-MIR & 380–14,286 & 26,315–700 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected spectral range in reflectance spectroscopy.  
Figure 2: Wavelength of different spectral ranges.  
Meanwhile, Ref. [44] aimed to predict soil organic carbon using a local PLSR approach. They amassed nearly 20,000 samples from across the European Union, scanned using a VNIR spectrometer. Their results highlighted a promising predictive capability for mineral soils, with RMSE values of 3.6 g/kg for cropland and 7.2 g/kg for grassland.  
An innovative calibration method, known as regression rules, was introduced by [45]. This approach offers benefits such as high accuracy, simplicity in interpretation, variable selection, parsimony, and adherence to the upper and lower prediction boundaries.  
Lastly, Ref. [46] set out to calibrate and validate models for TC and TOC using approximately 20,000 samples from the Rapid Carbon Assessment Project (RaCA). This nationwide initiative gathered over 144,000 soil samples from across the U.S for carbon stock mapping using VisNIR. Models were developed using either PLSR or Artificial Neural Network (ANN). The results demonstrated that the ANN-calibrated models for OC and TC, with \(R^{2}\) values exceeding 0.94, had a significant edge over the PLSR models, which had an \(R^{2}\) value of 0.83. This suggests potential benefits in combining neural networks with reflectance spectroscopy.