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A peer-reviewed article of this preprint also exists.
This version is not peer-reviewed
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 &VRI modules | Applicable | Applicable | Not applicable | Applicable |
Agrivi | Applicable, Plan, monitor and analyze crop activities | Applicable, Crops seasons & Pest monitor alert | Applicable | Applicable, Crop data & 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 & VRI Open Data, Farmers/Providers & 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 & 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 | lies on its 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 observes is needed to avoid full state measurement | Recommended |
Adaptive | improve 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 ( | Moisture in sounding (%) | Pumps flow (%) | Outflows(Q/se) | Valve opening(rad/sec) |
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 (sec) | 0.8 | 1.2 | 33.3 |
Settling time (sec) | 0.012 | 1.04 | 98.8 |
Peak time (sec) | 0.13 | 1.2 | 89.2 |
Temperature (Celsius) | 27 | 27 | 0 |
ADH values | 750 | 750 | 0 |
Moisture in sounding (%) | 34.72 | 30 | 15.7 |
Pumps flow (%) | 4.494 | 3.5 | 28.4 |
Outflows(Q/se) | 0.025 | 0.025 | 0 |
Valve opening(rad/sec) | 0.5 | 0.33 | 51.51 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
Submitted:
19 January 2024
Posted:
19 January 2024
You are already at the latest version
A peer-reviewed article of this preprint also exists.
This version is not peer-reviewed
Submitted:
19 January 2024
Posted:
19 January 2024
You are already at the latest version
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 &VRI modules | Applicable | Applicable | Not applicable | Applicable |
Agrivi | Applicable, Plan, monitor and analyze crop activities | Applicable, Crops seasons & Pest monitor alert | Applicable | Applicable, Crop data & 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 & VRI Open Data, Farmers/Providers & 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 & 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 | lies on its 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 observes is needed to avoid full state measurement | Recommended |
Adaptive | improve 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 ( | Moisture in sounding (%) | Pumps flow (%) | Outflows(Q/se) | Valve opening(rad/sec) |
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 (sec) | 0.8 | 1.2 | 33.3 |
Settling time (sec) | 0.012 | 1.04 | 98.8 |
Peak time (sec) | 0.13 | 1.2 | 89.2 |
Temperature (Celsius) | 27 | 27 | 0 |
ADH values | 750 | 750 | 0 |
Moisture in sounding (%) | 34.72 | 30 | 15.7 |
Pumps flow (%) | 4.494 | 3.5 | 28.4 |
Outflows(Q/se) | 0.025 | 0.025 | 0 |
Valve opening(rad/sec) | 0.5 | 0.33 | 51.51 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
Chung-Liang Chang
et al.
Sensors,
2015
Arunesh Singh
et al.
Energies,
2022
Konstantinos Tatas
et al.
Technologies,
2022
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