The most widespread use of automated irrigation scheduling systems is in the intensive horticultural, and especially the protected cropping, sector. In general, the automated systems in common use are based on simple automated timer operation, or in some cases the signal is provided by soil moisture sensors. For timer-based operation many systems simply aim to provide excess water to runoff at intervals (e.g. flood-beds or capillary matting systems), although some at least attempt to limit water application by only applying enough to replenish evaporative losses (often calculated from measured pan evaporation; Allen et al., 1999). Much greater sophistication is required if an objective is to improve the overall irrigation water use efficiency or to apply an RDI system. Most of the remaining automated systems currently in operation base control on soil moisture sensing; at least this approach has the potential for greater precision and improved water use efficiency.
Applications of automated plant-based sensing are largely in the developmental stage, partly because it is usually necessary to supplement the plant-stress sensing by additional information (such as evaporative demand). In principle, with high-frequency on-demand irrigation systems one could envisage a real-time control system where water supply is directly controlled by a feedback controller operated by the stress sensor itself, so that no information on the required irrigation amount is needed. For such an approach care will be necessary to take account of any lags in the plant physiological response used for the control signal.
The use of expert systems (Plant et al., 1992), which integrate data from several sources, appears to have great potential for combining inputs from thermal or other crop response sensors and environmental data for a water budget calculation to derive a robust irrigation schedule.
Among the various plant-based sensors that have been incorporated into irrigation control systems are stem diameter gauges (Huguet et al., 1992), sap-flow sensors (Schmidt and Exarchou, 2000) and acoustic emission sensors (Yang et al., 2003), though there has been most interest in the application of thermal sensors. For example, Kacira and colleagues (Kacira and Ling, 2001; Kacira et al., 2002) have developed and tested on a small scale an automated irrigation controller based on thermal sensing of plant stress. Similar approaches have been applied in the field: for example, Evans et al. (2001) and Sadler et al. (2002) mounted an array of 26 infrared thermometers (IRTs) on a centre pivot irrigation system which they used to monitor irrigation efficiency, but had not developed the system to a stage where it could be used for fully automated control. Colaizzi et al. (2003) have tested another system that includes thermal sensing of canopy temperature on a large linear move irrigator (where the irrigator moves across the field). In another approach to the use of canopy temperature that makes use of the ‘thermal kinetic window’, Upchurch et al. (1990) and Mahan et al. (2000) have developed what they call a ‘biologically identified optimal temperature interactive console’ for the control of trickle and other irrigation systems based on canopy temperature measurements. In this direct control system, irrigation is applied as canopy temperature exceeds a crop-specific optimum. The development of thermal infrared imaging methods of irrigation control will be aided by the recent development of automated image analysis systems for extraction of the temperatures of leaf surfaces from thermal images, including shaded and sunlit leaves, soil, and other surfaces (Leinonen and Jones, 2004).