Utilizing an Internet of Things (IoT) Device, Intelligent Control Design, and Simulation for an Agricultural System
Abstract
:1. Introduction
2. Literature Review
3. Mathematical Models of the System
3.1. Working Principles of Smart Agriculture
3.2. Mathematical Models of the Water Reservoir Tank Level, and Its Opening Valve
3.3. Fuzzy Logic Control Design
3.3.1. Membership Function Assignments
3.3.2. Case Study for the Intelligent Fuzzy Logic Model
- Temperature:
- ◦
- If the temperature is low, then the output is cold;
- ◦
- If the temperature is normal, then the output is moderate;
- ◦
- If the temperature is high, then the output is hot.
- Humidity:
- ◦
- If the humidity is low, then the output is dry;
- ◦
- If the humidity is normal, then the output is moderate;
- ◦
- If the humidity is high, then the output is wet.
- Soil Moisture:
- ◦
- If the soil moisture is low, then the output is dry;
- ◦
- If the soil moisture is normal, then the output is moderate;
- ◦
- If the soil moisture is high, then the output is wet.
- Soil Acidity:
- ◦
- If the soil acidity is low, then the output is basic;
- ◦
- If the soil acidity is normal, then the output is neutral;
- ◦
- If the soil acidity is high, then the output is acidic.
3.3.3. Formulation of the Air Properties in the Agricultural Field
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Technologies | Goals | Business Processing Module | Alarm Notification Module | Data Control | Communication Protocol | Posting On Social Networks and Public Data |
---|---|---|---|---|---|---|
APOLLO | Controlling crop growth and conditions | Applicable, VRI estimation module | Applicable | Applicable, crop growth monitoring and crop yield estimation | Not applicable | Not applicable |
SMART AKIS | Management Information | Not applicable | Not applicable | Applicable, flexible and adaptive platform of smart farming technologies | Not applicable | Applicable |
SIG AGRO ASESOR | Crop SIG Manager | Applicable, VRF and VRI modules | Applicable | Applicable | Not applicable | Applicable |
Agrivi | Applicable, Plan, monitor and analyze crop activities | Applicable, crop seasons and pest monitor alert | Applicable | Applicable, crop data and inputs cost management | Not applicable | Not applicable |
Smart Water-Saving | Intelligent irrigation programmer with sensory connectivity | Not applicable | Not applicable | Applicable, soil moisture data acquisition | Applicable, private protocol | Not applicable |
PLATEM PA | Applicable, Management Information with an intelligent VRF and VRI Open Data, Farmers/Providers and Social network | Applicable, business rule engine based on data acquisition and historical data | Applicable, notification module in multimedia platform | Applicable, historical data acquisition is represented in graphs and downloaded files | Communication protocol Implemented based on open standard protocol in VRF and VRI devices | Farmers and providers access on-line forums to post results, crop failures, alerts, crop yield, |
Controller Algorithms | Advantages | Disadvantages | Applicable in Smart Agriculture? |
---|---|---|---|
PID | Simplicity, applicability, and reliability | Long tuning time | Yes, recommended |
P | Easy to implement | Long settling time, steady state error | Partially, recommended |
PD | Easy to stabilize, faster response than just P controller | Can amplify high frequency noise | Partially, recommended |
PI | No steady state error | Narrower range of stability | Partially, recommended |
MPC | Works effectively within constraints of the real actuator which are relatively narrow | Depends on complex algorithm that needs longer time than the other controller | Recommended |
LQR | Simplicity, robustness, and flexibility | Only requires the knowledge of the system dynamics and the desired cost function. It does not depend on the initial conditions, disturbances, or uncertainties of the system | Recommended |
Fuzzy logic | Flexibility, ease of implementation, robustness, and interpretability | Dependence on human expertise, difficulty in tuning, limited accuracy and computational complexity | Best recommended |
Slide mode | Fast dynamic response, insensitivity to variations in plant parameters and external disturbance. | Chattering, which is a very high-frequency oscillation of the sliding variable around the sliding manifold | Recommended |
Backstepping | Can accurately track the desired trajectory or setpoint, ensuring that the system behaves as intended | The high gain observed is needed to avoid full state measurement | Recommended |
Adaptive | Improves performance and robustness | High cost is produced and the process is very complex. | Recommended |
Machine learning | Improved accuracy, cost reduction, scalability, increased efficiency, data dependency, computational resources, sampling | Needs high training | Recommended |
Specification of the Membership | Comparisons of the Memberships | Level of Estimating Probability |
---|---|---|
Singleton membership function | This assigns a membership value of 1 to a specific value of x. It is useful when the set has a single element [15]. | Good |
Triangular membership function | This is one of the most widely used membership functions. It is used to model sets that have a triangular shape. The membership value increases linearly from 0 to 1 and then decreases linearly from 1 to 0 [16]. | Better |
Trapezoidal membership function | This is similar to the triangular membership function, but it has a flat top. It is used to model sets that have a trapezoidal shape [17]. | Better |
Gaussian membership function | This is used to model sets that have a bell-shaped curve. It is often used in statistics to model normal distributions [18]. | Excellent |
Sigmoidal membership function | This is used to model sets that have an S-shaped curve. It is often used in artificial neural networks [19]. | Better |
Generalized bell membership function | This is a generalization of the Gaussian membership function. It is used to model sets that have a bell-shaped curve, but with more flexibility [17]. | Best |
Z-shaped membership function | This is used to model sets that have a Z-shaped curve. It is often used in control systems [18] | Better |
Temperature | Opening Valve | |||
---|---|---|---|---|
Wet | Cold | Moderate | Dry | |
Low | N2 | Z | P1 | P2 |
Normal | Z | Z | P1 | P2 |
High | P2 | P1 | Z | P2 |
Input Parameters | Output Parameters | ||||||
---|---|---|---|---|---|---|---|
Temperature (Celsius) | Water Vapor per Kg | ADH Values | Water Level Sensors (cm3) | Moisture in Sounding (%) | Pump Flow (%) | Outflow (Q/s) | Valve Opening (rad/s) |
27 | 10 | 750 | 73.31 | 34.72 | 4.494 | 0.025 | 0.5 |
20 | 10 | 500 | 48.88 | 45.87 | 3.275 | 0.025 | 0.5 |
10 | 10 | 300 | 29.33 | 84.75 | 1.424 | 0.025 | 0.5 |
0 | 0 | 50 | 4.888 | 555.5 | 6.718 | 0.025 | 0.5 |
0 | 20 | 50 | 4.888 | 1111 | 6.718 | 0.03082 | 0.5 |
40 | 10 | 1000 | 97.75 | 23.92 | 4.008 | 0.025 | 0.5 |
Specification Parameters | Current Work | Previous Work [2,3,4] | Change (%) (Current Work over Previous Work) |
---|---|---|---|
Rise time (s) | 0.8 | 1.2 | 33.3 |
Settling time (s) | 0.012 | 1.04 | 98.8 |
Peak time (s) | 0.13 | 1.2 | 89.2 |
Temperature (Celsius) | 27 | 27 | 0 |
ADH values | 750 | 750 | 0 |
Moisture in sounding (%) | 34.72 | 30 | 15.7 |
Pump flow (%) | 4.494 | 3.5 | 28.4 |
Outflow (Q/s) | 0.025 | 0.025 | 0 |
Valve opening (rad/s) | 0.5 | 0.33 | 51.51 |
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Amertet Finecomess, S.; Gebresenbet, G.; Alwan, H.M. Utilizing an Internet of Things (IoT) Device, Intelligent Control Design, and Simulation for an Agricultural System. IoT 2024, 5, 58-78. https://doi.org/10.3390/iot5010004
Amertet Finecomess S, Gebresenbet G, Alwan HM. Utilizing an Internet of Things (IoT) Device, Intelligent Control Design, and Simulation for an Agricultural System. IoT. 2024; 5(1):58-78. https://doi.org/10.3390/iot5010004
Chicago/Turabian StyleAmertet Finecomess, Sairoel, Girma Gebresenbet, and Hassan Mohammed Alwan. 2024. "Utilizing an Internet of Things (IoT) Device, Intelligent Control Design, and Simulation for an Agricultural System" IoT 5, no. 1: 58-78. https://doi.org/10.3390/iot5010004