Dynamic Modeling of Crop–Soil Systems to Design Monitoring and Automatic Irrigation Processes: A Review with Worked Examples

Abstract

The smart use of water is a key factor in increasing food production. Over the years, irrigation has relied on historical data and traditional management policies. Control techniques have been exploited to build automatic irrigation systems based on climatic records and weather forecasts. However, climate change and new sources of information motivate better irrigation strategies that might take advantage of the new sources of information in the spectrum of systems and control methodologies in a more systematic way. In this connection, two open questions deserve interest: (i) How can one deal with the space–time variability of soil conditions? (ii) How can one provide robustness to an irrigation system under unexpected environmental change? In this review, the different elements of an automatic control system are described, including the mathematical modeling of the crop–soil systems, instrumentation and actuation, model identification and validation from experimental data, estimation of non-measured variables and sensor fusion, and predictive control based on crop–soil and weather models. An overview of the literature is given, and several specific examples are worked out for illustration purposes.

Deep learning-based method for real-time spinach seedling health monitoring

“…This method does not achieve fully automated smart irrigation and increases labor cost. Lopez-Jimenez et al 9 derived the current soil water content by obtaining environmental conditions, such as environmental temperature, weather conditions, etc., through wireless sensors. This method enables monitoring of the crop growth period and automated irrigation.…”

Irrigation Distribution Network Design Parameters and Their Influence on Sustainability Management

“…There are models, which consider the soil-water process, reflected in crop water space-time requirements and water balance [87]. They include calculating the inputs and outputs of the system, effective rainfall, evapotranspiration, and crop requirements using soil, climate, and crop data [88].…”

Application Predictive Control Strategies Based on Models for Optimal Irrigation of Andean Crops

“…However, they are limited and do not consider the specific conditions of the crop, given the complexity of agricultural systems (non-linearity, multivariate). MPC strategies have shown superior performance compared to classical control strategies [2,3]. This controller is based on three ideas: the use of a prediction model, the optimization on a sliding horizon, and feedback adjustment [4].…”