Alison McCarthy

Advanced process control of irrigation: the current state and an analysis to aid future development

Control engineering approaches may be applied to irrigation management to make better use of available irrigation water. These methods of irrigation decision-making are being developed to deal with spatial and temporal variability in field properties, data availability and hardware constraints. One type of control system is advanced process control which, in an irrigation context, refers to the incorporation of multiple aspects of optimisation and control. Hence, advanced process control is particularly suited to the management of site-specific irrigation. This paper reviews applications of advanced process control in irrigation: mathematical programming, linear quadratic control, artificial intelligence, iterative learning control and model predictive control. From the literature review, it is argued that model-based control strategies are more realistic in the soil–plant–atmosphere system using process simulation models rather than using ‘black-box’ crop production models. It is also argued that model-based control strategies can aim for specific end of season characteristics and hence may achieve optimality. Three control systems are identified that are robust to data gaps and deficiencies and account for spatial and temporal variability in field characteristics, namely iterative learning control, iterative hill climbing control and model predictive control: from consideration of these three systems it is concluded that the most appropriate control strategy depends on factors including sensor data availability and grower’s specific performance requirements. It is further argued that control strategy development will be driven by the available sensor technology and irrigation hardware, but also that control strategy options should also drive future plant and soil moisture sensor development.

Managing Spatial and Temporal Variability in Irrigated Agriculture through Adaptive Control

Abstract Spatial variability in crop production occurs as a result of spatial and temporal variations in soil structure and fertility; soil physical, chemical and hydraulic properties; irrigation applications; pests and diseases; plant genetics; and local microclimate. This review paper argues that infield variability can be managed and the efficiency of irrigation water use increased by spatially variable application of irrigation water to meet the specific needs of individual management zones (areas of crop whose properties are relatively homogenous). Key areas identified requiring interdisciplinary research are the prescription of irrigated crop water requirements, strategies for quantifying and managing spatial variability, and the development of adaptive systems for control of water application at appropriate temporal intervals and spatial scales. Example strategies for the implementation of adaptive control for furrow irrigation and large mobile irrigation machines are described.

Real-time data requirements for model-based adaptive control of irrigation scheduling in cotton

Abstract Model-based adaptive control strategies can be used to determine site-specific irrigation volumes with the aim of maximising crop water use efficiencies and/or yield. These strategies require infield weather, soil and crop measurements to calibrate a crop model: the crop model is then used to determine the irrigation applications throughout the crop season which produce the desired simulated crop response or condition (eg. maximum yield). However, data collection spatially over a field and throughout the crop season will potentially lead to a large sensed data requirement which may be impractical in a field implementation. Not all the collected data may be required to sufficiently calibrate the crop model and determine irrigation applications for model-based adaptive control; rather, a smaller dataset consisting of only the most influential sensor variables may be sufficient for adaptive control purposes. This paper reports on afield study which examined the utility of five sensed variables – evaporative demand, soil moisture, plant height, square count and boll count – to calibrate the cotton model OZCOT within a model-based controller and evaluate the relative significance of each sensed variable (either individually or in combination) as a control input. For the field study conditions, OZCOT was most effectively calibrated (and therefore able to predict the soil and crop response to irrigation application) using full data input, while for situations where only two data inputs were available, the simulations suggested that either weather-and-plant or soil-and-plant inputs were preferable.

Holistic control system design for large mobile irrigation machines

Large mobile irrigation machines are self-propelled sprinkler irrigation systems which farmers are rapidly adopting due to the high precision of the irrigation application. Although it is highly desirable that control systems be used with such machines to both optimise the irrigation water volume applied to field crops and optimise water use efficiency, there are difficulties in applying classical control techniques. These are caused principally by the very slow speed of crop growth-response and stress-response dynamics; but in addition characteristics of the plant which are poorly known and in-field sensors which provide only sparse, low-quality data for feedback control.