Survey for Soil Sensing with IOT and Traditional Systems
Abstract
:1. Introduction
2. Tradicitonal Soil Sensing System
2.1. Fundamentals of Soil Reflectance Spectroscopy
2.2. Soil Sample Pretreatment
2.2.1. Preparation of Soil Samples
2.2.2. Pre-Processing Steps for Soil Samples
2.3. Spectral Range Selection for Reflectance Spectroscopy
2.4. Total Carbon and Total Organic Carbon
2.5. Soil Moisture
2.6. Soil Macronutrients (N, P and K)
3. RF-Based Soil Sensing Systems in Internet of Things (IoT)
3.1. Wi-Fi Based Soil Sensing Systems
3.2. RFID-Based Soil Sensing Systems
3.3. LTE-Based Soil Sensing Systems
3.4. LoRa-Based Soil Sensing Systems
4. Inference Method and Evaluation Metrics
4.1. Method for Spectral Information Analysis
4.1.1. Principal Component Regression
4.1.2. Partial Least Square Regression
4.1.3. Uninformative Variable Elimination Method
4.1.4. Least Squares Support Vector Regressions
4.2. Evaluation Metrics
5. Future Aspect
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Work | Year | Sample Preparation | Category of Properties | Signal Types | Calibration Method | Device & Environment | Applications |
---|---|---|---|---|---|---|---|
[17] | 2006 | ∘ | 🟉 | ∘ | ∘ | ∘ | |
[18] | 2009 | ∘ | 🟉 | ∘ | ∘ | ∘ | |
[19] | 2010 | 🟉 | 🟉 | ∘ | ∘ | ∘ | ∘ |
[20] | 2014 | 🟉 | 🟉 | ∘ | ∘ | ∘ | |
[21] | 2018 | 🟉 | ∘ | 🟉 | 🟉 | ||
[22] | 2018 | ∘ | ∘ | ∘ | ∘ | ||
[7] | 2022 | ∘ | 🟉 | ∘ | 🟉 | ∘ | 🟉 |
This Paper | 2023 | 🟉 | ∘ | 🟉 | 🟉 | 🟉 | 🟉 |
Spectral Range | Wavelength (nm) | Wave Number (cm−1) |
---|---|---|
Visible | 380–800 | 26,315–12,500 |
NIR | 800–2500 | 12,500–4000 |
MIR | 2500–25,000 | 4000–400 |
VIS-NIR | 380–2400 | 26,315–4167 |
VIS-NIR-MIR | 380–14,286 | 26,315–700 |
Work | Year | Category | Preprocessing | Performance in | Calibration Method | Spectral Range |
---|---|---|---|---|---|---|
Total Carbon (TC) and Total Organic Carbon | ||||||
[39] | 2003 | TOC | Required | 0.9995 | Regression | VNIR |
[42] | 2012 | TC | Required | 0.95 | PLSR | VNIR & MIR |
[44] | 2014 | TOC | Required | 0.85 | PLSR | VNIR |
[46] | 2016 | TC/TOC | Required | 0.94 | Artificial Neural Network (ANN) | VNIR |
[43] | 2019 | TOC | Required | 0.86 | PLSR | VNIR & MIR |
Moisture | ||||||
[50] | 2002 | Moisture | Required | 0.975 | Log | VNIR |
Macronutrients | ||||||
[35] | 2011 | N | Required | 0.90 | PLSR/LS-SVR | NIR/MIR |
P | Required | 0.88 | ||||
K | Required | 0.89 | ||||
[33] | 2020 | N | Required | 0.91 | Linear/Unique Curve | VNIR |
P | Required | 0.99 | ||||
K | Required | 0.99 | ||||
[34] | 2019 | N | Required | 0.87 | PCR/PLSR | NIR |
P | Required | 0.99 | ||||
K | Required | 0.90 | ||||
[61] | 2020 | N | Required | 0.94 | PLSR | VNIR |
[63] | 2013 | P | Required | 0.86 | PLSR | VNIR |
Work | Category | Signal Type | Cost | Covering Range | Central Frequency | Energy Consumption | Capacity of Node | Error |
---|---|---|---|---|---|---|---|---|
[12] | Moisture | Wi-Fi | Less than 100 | Up to 30 cm depths | 2.4 GHz | High | Low | Less than 3% |
[13] | Moisture | Wi-Fi | Less than 100 | Up to 38 cm depths | 2.5 GHz | High | Low | 1.1% |
[14] | Moisture | RFID | Low | 2 m | 902.75–927.25 MHz | Extremly Low | High | 5% |
[15] | Moisture | LTE | 55 | 2.4 km | 700–800 MHz | Medium | Low | 3.15% |
[16] | Moisture | LoRa | 7.5 for LoRa Switch | 100 m | 915 MHz | Low | High | 3.1% |
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Wang, J.; Zhang, X.; Xiao, L.; Li, T. Survey for Soil Sensing with IOT and Traditional Systems. Network 2023, 3, 482-501. https://doi.org/10.3390/network3040021
Wang J, Zhang X, Xiao L, Li T. Survey for Soil Sensing with IOT and Traditional Systems. Network. 2023; 3(4):482-501. https://doi.org/10.3390/network3040021
Chicago/Turabian StyleWang, Juexing, Xiao Zhang, Li Xiao, and Tianxing Li. 2023. "Survey for Soil Sensing with IOT and Traditional Systems" Network 3, no. 4: 482-501. https://doi.org/10.3390/network3040021