Smart & Green: An Internet-of-Things Framework for Smart Irrigation
Abstract
:1. Introduction
2. Related Work
- What software exists for agricultural management that automates the process of gathering, preprocessing, fusing, and synchronizing the data used in irrigation management?
- Does the software forecast the soil moisture?
- Can users configure the software using information about their crops, irrigation system, soil sensors, and weather stations close to the monitored field?
2.1. Data Gathering
2.2. Data Preprocessing
2.3. Irrigation Management
2.4. Soil Moisture Prediction
2.5. IoT Platforms for Smart Agriculture
3. Proposal
3.1. Application Layer
3.1.1. Irrigation Management Automatization
- User register: Smart&Green framework allows two types of users: Specialist and regular. Specialist users can provide agronomic information, such as different types of crops, soil, and irrigation system features. They can also register outlier removal criteria for weather and soil data and choose algorithms for the fusion service. Regular users can register farms and fields.
- Farm Register: Users provide information about the farm, such as address and geographical co-ordinates (i.e., latitude and altitude) for the Smart&Green framework, and select the weather station closest to the farm.
- Crop register: A specialist user can create types of crops using information such as a description, the curve of the crop coefficient, and the critical moisture condition.
- System Irrigation Register: A specialist user inserts the type of irrigation systems, such as “micro-sprinkler”.
- Weather Station Register: Smart&Green automatically selects the weather station closest to the farm using the geographical co-ordinates. Users can confirm this or choose another one.
- Soil Sensor Register: The user can insert the types of soil sensors used. Smart&Green already has analog and digital tensiometers.
- Field Register: Users set the field configuration features (Figure A1), such as the type of crop, irrigation system, soil, and if there are soil monitoring points. The developmental stage of the crop represents the number of days since the initial cultivation. Effective precipitation (mm/h) and efficiency of the irrigation system are necessary for irrigation management, in order to compute the irrigation time. In the case of monitoring points, users can register the type of soil moisture sensor (analog or digital) and the depth z monitored for each monitoring point in the field.
- Field Communication register: In the case where the monitored fields have sensor nodes that automatically gather soil data, users can define the type of communication to send the data to the framework. Users set the IP address and specific configuration (CoAP or MQTT).
- Outlier Removal Criteria Register: Specialist users can create a threshold for minimum and maximum values of each type of weather and soil moisture data used in irrigation management.
3.1.2. Soil Moisture Prediction
3.2. Service Layer
3.3. Communication Layer
4. Materials and Method
4.1. Smart & Green Framework Implementation
4.2. Soil Moisture Prediction
4.2.1. Raw Data Set
4.2.2. Outlier Detection and Removal
4.2.3. Machine Learning Methods
5. Results
5.1. Impact of Outlier Removal Techniques on Real Moisture Data by Irrigation Management
5.2. Performance of Models for Soil Moisture Prediction
5.3. Analysis of Water Savings through the Use of Predicted Data of Soil Moisture
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A. Irrigation Management
- represent the thickness of soil along the profile (monitored depth);
- represents the field capacity at a depth z after the drainage of water excess, which is a constant obtained by a laboratory soil analysis;
- is the ideal moisture for irrigation, signaling when the crop productivity starts to decline;
- is the current soil moisture. In matric potential management, we initialize the irrigation by ; and
- is the efficiency of the irrigation system used in the field.
Appendix B. The Problem of Soil Moisture Prediction
- and are the minimum and maximum air temperature, respectively;
- and are the minimum and maximum relative humidity, respectively;
- is the net radiation;
- is the wind speed;
- P is the atmospheric pressure; and
- is the rainfall.
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Acronym | Description | Unit |
---|---|---|
Tx–y | Tensiomenter reading at y depth in a point of monitoring x | kPa |
Wx | The water amount given to the crop field in a point of monitoring x through irrigation | L |
T_max | maximum temperature of air | |
T_min | minimum temperature of air | |
RH_max | maximum relative humidity | % |
RH_min | minimum relative humidity | % |
Rn | net radiation | MJmd |
U2 | wind speed | m/s |
P | atmospheric pressure | kPa |
Ri_f | rainfall gathered by the pluviometer sensor | mm |
Kc | crop coefficient |
n | |||
---|---|---|---|
0.14010 | 0.38839 | 0.022504 | 20.524 |
Irrigation Management | Outlier Removal Technique | Mean (%) | Confidence Interval (90%) |
---|---|---|---|
Water Balance | None | 90.4 | (81.6, 99.1) |
Water Balance | Zscore | 89.3 | (80.4, 98.3) |
Water Balance | MZscore | 89.3 | (80.4, 98.3) |
Water Balance | GESD | 90.4 | (81.6, 99.1) |
Water Balance | Chauvenet | 90.4 | (81.6, 99.1) |
Matric Potential | Zscore | 20.7 | (13.7, 27.6) |
Matric Potential | MZscore | 14.1 | (8.1, 20.1) |
Matric Potential | GESD | 4.3 | (0.8, 7.9) |
Matric Potential | Chauvenet | 5.4 | (1.5, 9.3) |
Algorithm | MAE | Conf. Interval MAE | RMSE | Conf. Interval RMSE |
---|---|---|---|---|
Linear Regression | 0.1408 | (0.1318, 0.1498) | 0.1730 | (0.1642, 0.1818) |
Decision Stump | 0.1798 | (0.1632, 0.1965) | 0.2196 | (0.2031, 0.2360) |
M5P | 0.1288 | (0.1159, 0.1416) | 0.1722 | (0.1576, 0.1868) |
Random Tree | 0.1443 | (0.1319, 0.1567) | 0.2120 | (0.1877, 0.2363) |
Random Forest | 0.1189 | (0.1025, 0.1352) | 0.1551 | (0.1393, 0.1709) |
RepTree | 0.1227 | (0.1119, 0.1336) | 0.1684 | (0.1566, 0.1801) |
Gradient Boosting | 0.0752 | (0.0683, 0.0822) | 0.1038 | (0.0939, 0.1137) |
Algorithm | MAE | Conf. Interval MAE | RMSE | Conf. Interval RMSE |
---|---|---|---|---|
Linear Regression | 0.1628 | (0.1510, 0.1746) | 0.1993 | (0.1881, 0.2110) |
Decision Stump | 0.1938 | (0.1812, 0.2063) | 0.2335 | (0.2220, 0.2450) |
M5P | 0.1461 | (0.1348, 0.1573) | 0.1824 | (0.1706, 0.1942) |
Random Tree | 0.1494 | (0.1413, 0.1574) | 0.2094 | (0.2004, 0.2183) |
Random Forest | 0.1406 | (0.1317, 0.1494) | 0.1873 | (0.1769, 0.1977) |
RepTree | 0.1438 | (0.1362, 0.1515) | 0.1832 | (0.1740, 0.1924) |
Gradient Boosting | 0.1382 | (0.1382, 0.1382) | 0.1717 | (0.1717, 0.1717) |
Irrigation | Prediction | Outlier Removal | Mean | Confidence Interval |
---|---|---|---|---|
Management | Approach | Technique | (%) | (90%) |
Water Balance | Global | None | 56.4 | (41.4, 71.5) |
Water Balance | Local | None | 100 | (100, 100) |
Water Balance | Local | Zscore | 90.0 | (85.7, 94.3) |
Water Balance | Local | MZscore | 75.6 | (67.1, 84.2) |
Water Balance | Local | GESD | 100 | (100, 100) |
Water Balance | Local | Chauvenet | 90.0 | (85.7, 94.3) |
Matric Potential | Global | None | 53.1 | (44.5, 61.7) |
Matric Potential | Local | None | 43.3 | (34.7, 51.9) |
Matric Potential | Local | Zscore | 95.6 | (92.1, 99.2) |
Matric Potential | Local | MZscore | 97.8 | (95.3, 100) |
Matric Potential | Local | GESD | 43.3 | (34.7, 51.9) |
Matric Potential | Local | Chauvenet | 62.9 | (54.6, 71.3) |
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G. S. Campos, N.; Rocha, A.R.; Gondim, R.; Coelho da Silva, T.L.; Gomes, D.G. Smart & Green: An Internet-of-Things Framework for Smart Irrigation. Sensors 2020, 20, 190. https://doi.org/10.3390/s20010190
G. S. Campos N, Rocha AR, Gondim R, Coelho da Silva TL, Gomes DG. Smart & Green: An Internet-of-Things Framework for Smart Irrigation. Sensors. 2020; 20(1):190. https://doi.org/10.3390/s20010190
Chicago/Turabian StyleG. S. Campos, Nidia, Atslands R. Rocha, Rubens Gondim, Ticiana L. Coelho da Silva, and Danielo G. Gomes. 2020. "Smart & Green: An Internet-of-Things Framework for Smart Irrigation" Sensors 20, no. 1: 190. https://doi.org/10.3390/s20010190
APA StyleG. S. Campos, N., Rocha, A. R., Gondim, R., Coelho da Silva, T. L., & Gomes, D. G. (2020). Smart & Green: An Internet-of-Things Framework for Smart Irrigation. Sensors, 20(1), 190. https://doi.org/10.3390/s20010190