IoT-Based Systems for Soil Nutrients Assessment in Horticulture
Abstract
:1. Introduction
2. Techniques and Technologies for Soil Fertility Characterization
3. Information System for Soil Nutrients Assessment
4. IoT-Based System for Soil Nutrient Assessment
4.1. Smart Sensor Nodes
4.2. Embedded Software
4.3. Mobile App
4.4. Experimental Protocol
5. Results
6. Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Method | Type | Advantages | Disadvantages |
---|---|---|---|
Biological characterization | Qualitative | Easy to obtain data; may provide data on influence of external factors and spatial variability | May be affected by differences on evaluator perception and knowledge |
Plant analysis | Qualitative or semi-quantitative | Fast, easy to obtain data | Changes are observed when it is too late. |
Time consuming, some procedures are expensive | |||
Chemical and physical characterization | Quantitative | Precise, efficient | Some portable sensors have lower sensibility, sensitivity, and accuracy than laboratory assessment |
Essential Plant Elements | Beneficial Plant Elements | ||
---|---|---|---|
Organic Elements | Mineral Elements | Cobalt (Cb) | |
Carbon (C) | Macronutrients | Micronutrients | Silicon (Si) |
Hydrogen (H) | Primary | Boron (B) | Sodium (Na) |
Oxygen (O) | Nitrogen (N) | Chlorine (Cl) | |
Phosphorous (P) | Iron (Fe) | ||
Potassium (K) | Manganese (Mn) | ||
Secondary | Molybdenum (Mo) | ||
Calcium (Ca) | Nickel (Ni) | ||
Magnesium (Mg) | Zinc (Zn) | ||
Sulfur (S) |
Element | Chemical Symbol | Concentration (μmol g−1 DW) | Concentration (mg kg−1) |
---|---|---|---|
Nitrogen | N | 1.000 | 15,000 |
Potassium | K | 250 | 10,000 |
Calcium | Ca | 125 | 5000 |
Magnesium | Mg | 80 | 2000 |
Phosphorus | P | 60 | 2000 |
Sulphur | S | 30 | 1000 |
Chlorine | Cl | 3.0 | 100 |
Boron | B | 2.0 | 20 |
Iron | Fe | 2.0 | 100 |
Manganese | Mn | 1.0 | 50 |
Zinc | Zn | 0.3 | 20 |
Copper | Cu | 0.1 | 6 |
Nickel | Ni | 0.001 | 0.1 |
Molybdenium | Mo | 0.001 | 0.1 |
Soil Parameter | Measurements Range | Measurements Accuracy |
---|---|---|
Temperature | −40 °C–80 °C | ±0.4% of FS |
Electric Conductivity | 0–20 mS cm−1 | ±2% of FS |
Moisture content | 0–100% | ±2% of FS (0–50%) ±3% of FS (50–100%) |
pH | 3–9 | ±5% of FS |
N content | 1–1999 mg/Kg | ±2% of FS |
P content | 1–1999 mg/Kg | ±2% of FS |
K content | 1–1999 mg/Kg | ±2% of FS |
Measurement Site | Species |
---|---|
1 | Beaucarnea recurvata Lem. |
2 | Sequoia sempervirens (D. Don) Endl. |
3 | Encephalartos lebomboensis Verd. |
4 | Ficus macrophylla Pers. |
5 | Dracaena draco (L.) L. |
6 | Afrocarpus mannii (Hook.f.) C.N.Page |
7 | Araucaria bidwillii Hook. |
8 | Brahea edulis H.Wendl. ex S.Watson |
9 | Ceiba speciosa (A.St.-Hil.) Ravenna |
10 | Bauhinia variegata L. |
11 | Phytolacca dioica L. |
12 | Metrosideros excelsa Sol. ex Gaertn. |
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Postolache, S.; Sebastião, P.; Viegas, V.; Postolache, O.; Cercas, F. IoT-Based Systems for Soil Nutrients Assessment in Horticulture. Sensors 2023, 23, 403. https://doi.org/10.3390/s23010403
Postolache S, Sebastião P, Viegas V, Postolache O, Cercas F. IoT-Based Systems for Soil Nutrients Assessment in Horticulture. Sensors. 2023; 23(1):403. https://doi.org/10.3390/s23010403
Chicago/Turabian StylePostolache, Stefan, Pedro Sebastião, Vitor Viegas, Octavian Postolache, and Francisco Cercas. 2023. "IoT-Based Systems for Soil Nutrients Assessment in Horticulture" Sensors 23, no. 1: 403. https://doi.org/10.3390/s23010403
APA StylePostolache, S., Sebastião, P., Viegas, V., Postolache, O., & Cercas, F. (2023). IoT-Based Systems for Soil Nutrients Assessment in Horticulture. Sensors, 23(1), 403. https://doi.org/10.3390/s23010403