Model based predictive control strategy for water saving drip irrigation

Abioye, Emmanuel Abiodun; Abidin, Mastura Shafinaz Zainal; Buyamin, Salinda; Mohammed, Olatunji Obalowu; Otuoze, Abdulrahaman Okino; Oleolo, Ibrahim; Mayowa, Abioye

doi:10.1016/j.atech.2023.100179

Cited by 9 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Water Balance Models [74] combine the available observations with the fundamental knowledge to characterize the system's behavior through the application of scientific methodologies. According to Water Balance Models, evapotranspiration, capillary rise, deep percolation, and the surface runoff effect are examples of water outflows that contribute to changes in soil moisture content over time, as water inflows like irrigation and rainfall are [75].…”

Section: Decisions Layermentioning

confidence: 99%

Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?

Del-Coco,

Leo,

Carcagnì

2024

Information

View full text Add to dashboard Cite

The management of water resources is becoming increasingly important in several contexts, including agriculture. Recently, innovative agricultural practices, advanced sensors, and Internet of Things (IoT) devices have made it possible to improve the efficiency of water use. However, it is the application of control strategies based on advanced machine learning techniques that enables the adoption of smart irrigation scheduling and the immediate economic, social, and environmental benefits. This challenging research area has attracted the attention of many researchers worldwide, who have proposed several technological and methodological solutions. Unfortunately, the results of these scientific efforts have not yet been categorized in a thematic survey, making it difficult to understand how far we are from optimal water management based on machine learning. This paper fills this gap by focusing on smart irrigation systems with an emphasis on machine learning. More specifically, the generic structure of a smart agriculture system is presented, and existing machine learning strategies and available datasets are discussed. Furthermore, several open issues are identified, especially in the processing of long-term data, also due to the lack of corresponding annotated datasets. Finally, some interesting future research directions to be pursued in order to build scalable, domain-independent approaches are proposed.

show abstract

Section: Decisions Layermentioning

confidence: 99%

Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?

Del-Coco,

Leo,

Carcagnì

2024

Information

View full text Add to dashboard Cite

show abstract

“…Wang et al 48 have applied AI along with IoT technologies for better yielding crops, analyzing the soil, cultivation growing conditions, and coordination between the farmers for advancing agricultural aspects across the total food supply chain management at the worldwide level. Abioye et al 49 have developed an ML model for a predictive control strategy that can save water during drip irrigation. This can also adjust the soil moisture content and the scheduling of irrigation can be analyzed due to losses of water from the soil and plant due to environmental conditions.…”

Section: Brief State‐of‐art Report On Development Of Smart Drip Irrig...mentioning

confidence: 99%

Experimental performance of soil monitoring system using IoT technique for automatic drip irrigation

Jain,

Mukherjee,

Karmakar

et al. 2023

Int J Communication

View full text Add to dashboard Cite

SummaryThis paper proposes an IoT‐enabled soil monitoring system using wireless sensor network for automatic irrigation in agricultural applications, especially for lemongrass plants, where an automated control system is required for irrigation applications. This can solve the problem of the water crisis, which is faced by the farmers during the cultivation of the crop in the field. This controls the water supply in the irrigation process using an IoT communication system. A system architecture for soil monitoring and controlling irrigation using IoT technique is designed where the different sensors and actuators like humidity, soil moisture, temperature, pump, and so forth are connected with a node microcontrol unit and message queuing telemetry transport (MQTT) protocol for enhancing communication capabilities. This wireless sensor network gives feedback to the system. This provides automation by the on/off pump system during drip irrigation. The sensor data are displayed on a PC or mobile phone through wireless communication and an IoT cloud platform. An experimental testing setup is developed and the experimental performance of a soil monitoring system using IoT technique for automatic drip irrigation has been carried out and soil moisture data are also stored in a cloud server for analytics. The performance shows that the MQTT protocol sends data within 48 s to the IoT cloud so that the data can be acquired in a faster manner. This shows that this kind of soil monitoring system is suitable for automatic drip irrigation, which enhances the farming process and overcomes the water crises in the agricultural system by reducing the wastage of water.

show abstract

“…By varying the values of η and δ within these ranges, the authors aimed to explore different combinations and determine the optimal parameter values for the CBSO-PID algorithm. In this paper, they conducted verification experiments to evaluate the performance of the algorithm for different parameter settings [34].…”

Section: Optimization Parametersmentioning

confidence: 99%

Economical Design of Drip Irrigation Control System Management Based on the Chaos Beetle Search Algorithm

Zhang,

Song

2023

Processes

View full text Add to dashboard Cite

In the realm of existing intelligent drip irrigation control systems, traditional PID control encounters challenges in delivering satisfactory control outcomes, primarily owing to issues related to non-linearity, time-varying behavior, and hysteresis. In order to solve the problem of the unstable operation of the drip irrigation system in an intelligent irrigation system, this paper proposes chaotic beetle swarm optimization (CBSO) based on the BAS (beetle antennae search) longicorn search algorithm, with inertial weights, variable learning factors, and logistic chaos initialization improving global search capabilities. This was accomplished by formulating the optimization objective, which involved integrating the control input’s time integral term, the square term, and the absolute value of the error. Subsequently, PID parameter tuning was performed. In order to verify the actual effect of the CBSO algorithm on the PID drip irrigation control system, MATLAB was used to simulate and compare PID control optimized by the GA algorithm, PSO algorithm, and BSO (beetle search optimization) algorithm. The results show that PID control based on CBSO optimization has a short response time, small overshoot, and no oscillation in the steady state process. The performance of the controller is improved, which provides a basis for PID parameter setting for a drip irrigation control system.

show abstract

Model based predictive control strategy for water saving drip irrigation

Cited by 9 publications

References 34 publications

Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?

Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?

Experimental performance of soil monitoring system using IoT technique for automatic drip irrigation

Economical Design of Drip Irrigation Control System Management Based on the Chaos Beetle Search Algorithm

Contact Info

Product

Resources

About