Agrotechnology

Effectiveness on Irrigation Management by Employing Predictive Control and Data-Driven Approaches

Carrillo Montero^* Department of Agricultural Technology, University of Castilla-La Mancha, Albacete, Spain
^*Correspondence: Carrillo Montero, Department of Agricultural Technology, University of Castilla-La Mancha, Albacete, Spain, Email:

Received: 05-Jun-2023, Manuscript No. AGT-23-21937; Editor assigned: 08-Jun-2023, Pre QC No. AGT-23-21937 (PQ); Reviewed: 22-Jun-2023, QC No. AGT-23-21937; Revised: 29-Jun-2023, Manuscript No. AGT-23-21937 (R); Published: 06-Jul-2023, DOI: 10.35248/2168-9881.23.12.320

Description

Data-driven Model Predictive Control (MPC) is a potential technique for precision irrigation management, which aims to optimize water use efficiency and crop yield by adjusting irrigation inputs according to the dynamic conditions of the soil, plant and environment. Data-driven MPC relies on data collected from sensors and remote sensing to learn the model of the irrigation system and predict its future behavior under different scenarios. Based on the predictions, data-driven MPC can generate optimal irrigation schedules that minimize water losses and maximize crop water productivity. Data-driven MPC has been applied in various aspects of precision irrigation management.

Applications of data-driven model predictive control in precision irrigation management

• Irrigation canal control in Data-driven MPC can regulate the water flow and level in irrigation canals by controlling the gates and pumps based on the water demand and supply of different zones. Data-driven MPC can also account for uncertainties and disturbances such as rainfall, evaporation and seepage.

• Irrigation scheduling in Data-driven MPC can determine the optimal timing and amount of irrigation for each crop based on the soil moisture, crop water requirement, weather forecast and water availability. Data-driven MPC can also adapt to changes in crop growth stages, soil properties and irrigation efficiency.

• Stem water potential regulation in Data-driven MPC can control the stem water potential of grapevines by adjusting the drip irrigation rate based on the measurements of sap flow sensors. Data-driven MPC can also consider the effects of solar radiation, air temperature and humidity on the plant transpiration.

• Soil moisture regulation in Data-driven MPC can maintain the soil moisture within a desired range by controlling the sprinkler irrigation system based on the feedback from soil moisture sensors. Data-driven MPC can also account for the spatial variability of soil moisture and the impact of irrigation uniformity.

• Prediction of plant disturbances in Data-driven MPC can detect and predict plant stress caused by pests, diseases or nutrient deficiencies by using spectral reflectance data from remote sensing. Data-driven MPC can also suggest corrective actions such as applying pesticides, fungicides or fertilizers.

Benefits of Data-driven MPC in precision irrigation management

• Reducing water consumption in Data-driven MPC can save water by applying only the necessary amount of irrigation according to the crop water needs and avoiding over-irrigation or under-irrigation.

• Improving crop yield and quality in Data-driven MPC can enhance crop growth and development by providing optimal water stress levels and avoiding waterlogging or drought stress.

• Increasing operational efficiency in Data-driven MPC can automate the irrigation process by using real-time data and feedback control, reducing human intervention and labor costs.

• Adapting to uncertainties in Data-driven MPC can cope with uncertainties and disturbances such as weather variability, sensor noise or system faults by using robust optimization and fault detection methods.

Challenges and limitations of data-driven MPC in precision irrigation management

• Data availability and quality in Data-driven MPC requires sufficient and reliable data from sensors and remote sensing to learn the model of the irrigation system and predict its future behavior. However, data may be incomplete, inaccurate or corrupted due to sensor failures, communication errors or environmental interferences.

• Model complexity and scalability in Data-driven MPC needs to capture the complex and nonlinear dynamics of the irrigation system and its interactions with the soil, plant and environment. However, developing such models may be difficult or computationally expensive, especially for large-scale or heterogeneous systems.

• Control performance and stability in Data-driven MPC needs to ensure that the generated irrigation schedules are feasible, optimal and stable under different scenarios. However, achieving such performance may be challenging or impossible due to model uncertainties, constraints or nonlinearities.

Therefore, data-driven MPC requires further research and development to overcome these challenges and limitations and to realize its full potential for precision irrigation management. Some possible directions for future work are developing novel data-driven modeling techniques that can learn accurate and efficient models of the irrigation system from various sources of data, such as physical equations, historical data or expert knowledge. Integrating machine learning and artificial intelligence methods that can enhance the prediction and optimization capabilities of data-driven MPC by using advanced algorithms such as deep learning, reinforcement learning or evolutionary computation. Incorporating multi-objective and multi-criteria optimization methods that can balance multiple conflicting goals and preferences of data-driven MPC, such as water use efficiency, crop yield, environmental impact or economic cost. Evaluating and validating data-driven MPC methods by conducting experiments and simulations on realworld irrigation systems and comparing their performance with other existing or conventional methods.