# Survey for Soil Sensing with IOT and Traditional Systems
## 3 RF-Based Soil Sensing Systems in Internet of Things (IoT)  
In recent times, the convergence of soil property sensing and the Internet of Things (IoT) has garnered increasing interest. While the advent of IoT in soil sensing has been relatively recent, its potential impact on advancing smart agriculture has been profound. A pivotal application of this is intelligent irrigation, which not only enhances plant growth and quality, but also conserves water resources [67]. Current soil moisture sensors are often prohibitively expensive for individual pot deployment [68]. The essence of RF-based IoT soil sensing revolves around remotely monitoring soil moisture levels. This technological shift encompasses a range of RF signals, from Wi-Fi and RFID to LTE and LoRa.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Work** & **Year** & **Category** & **Preprocessing** & **Performance in \(R^{2}\)** & **Calibration Method** & **Spectral Range** \\ \hline \multicolumn{6}{c}{Total Carbon (TC) and Total Organic Carbon} \\ \hline
[39] & 2003 & TOC & Required & 0.9995 & Regression & VNIR \\ \hline
[42] & 2012 & TC & Required & 0.95 & PLSR & VNIR \& MIR \\ \hline
[44] & 2014 & TOC & Required & 0.85 & PLSR & VNIR \\ \hline
[46] & 2016 & TC/TOC & Required & 0.94 &
\begin{tabular}{c} Artificial Neural \\ Network (ANN) \\ \end{tabular} & VNIR \\ \hline
[43] & 2019 & TOC & Required & 0.86 & PLSR & VNIR \& MIR \\ \hline \multicolumn{6}{c}{Moisture} \\ \hline
[50] & 2002 & Moisture & Required & 0.975 & Log & VNIR \\ \hline \multicolumn{6}{c}{Macronutrients} \\ \hline
[35] & 2011 &
\begin{tabular}{c} N \\ P \\ K \\ \end{tabular} & Required & 0.90 & \multirow{2}{*}{PLSR/LS-SVR} & \multirow{2}{*}{NIR/MIR} \\
[35] & 2011 &
\begin{tabular}{c} N \\ P \\ K \\ \end{tabular} & Required & 0.88 & \\ \multicolumn{6}{c}{Redquired} & 0.89 & \\ \hline
[33] & 2020 &
\begin{tabular}{c} N \\ P \\ K \\ \end{tabular} & Required & 0.91 & \multirow{2}{*}{Linear/Unique Curve} & \multirow{2}{*}{VNIR} \\
[34] & 2019 &
\begin{tabular}{c} N \\ P \\ K \\ \end{tabular} & Required & 0.87 & \multirow{2}{*}{PCR/PLSR} & \multirow{2}{*}{NIR} \\
[34] & 2018 &
\begin{tabular}{c} N \\ P \\ K \\ \end{tabular} & Required & 0.99 & \\ \hline
[61] & 2020 &
\begin{tabular}{c} N \\ P \\ K \\ \end{tabular} & Required & 0.94 & PLSR & VNIR \\ \hline
[63] & 2013 &
\begin{tabular}{c} P \\ \end{tabular} & Required & 0.86 & PLSR & VNIR \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between soil sensing work in four main attributes.  
Compared to traditional soil sensing methods, which rely on intricate chemical spectral analysis and often entail elaborate preprocessing steps [32, 33, 35, 36, 69] or specialized equipment like spectrometers [33, 35, 36, 37, 63], the benefits of IoT in soil sensing are clear-cut. Firstly, the investment required for IoT systems is generally lower than that for commercial devices [68]. Modern IoT-based soil moisture sensors are cost-effective. While commercial alternatives often exceed $100, these systems are typically priced below that threshold, yet they deliver performance on par with premium devices. Secondly, the adaptability of IoT systems is commendable; they can be seamlessly integrated beneath the soil's surface [12, 13, 15, 16].  
Lastly, these systems are equipped with extensive communication capabilities and compatible with several types of soil surfaces as shown in Figure 3. For instance, the system in [16] boasts a communication range of 100 ms, while [15] extends this to an impressive 2.4 km.