Water is essential for our daily life and is at the core of sustainable development. While about 70% of the Earth’s surface is covered by water, less than 2.5% of it is freshwater. The global water needs of the fast growing world population are already exceeding the currently available fresh water supply. The largest use of fresh water is in agriculture, which accounts for about 70% of the global fresh water consumption. As population growth continues, 60% more food will be needed to satisfy the demand of more than 9 billion people worldwide by 2050. However, in many regions (even the ones in water-rich countries such as USA and Canada), water allocated to irrigation is largely capped or will be capped in the near future and any further increase in irrigated acres needs to come essentially from improvements in water-use efficiency. The irrigation water-use efficiency worldwide is currently very low (around 50% to 60%). It is clear that policies related to water conservation and new technologies for more efficient water consumption need to be developed.
In the current practice, the amount of water to be irrigated and the time to apply the irrigation are determined in advance based on the irrigator’s (typically the farmer’s) knowledge. The actual conditions in the field are generally not considered in determining the irrigation amount and time. Thus from a process systems engineering (PSE) perspective, the current irrigation practice is an open-loop decision making process. It is well recognized in process control that open-loop control is quite imprecise. It is indeed one of the main causes of the low irrigation water-use efficiency.
To meet the challenges of water sustainability, the key is to close the decision-support loop to form a closed-loop system for precision irrigation. In the closed-loop system, sensing instruments (e.g., soil moisture and nutrient sensors, evapotranspiration (ET) gauge, thermal cameras on drones or satellite imagery data) can be used to collect various real-time field information regularly. The various field information can then be fused together to get estimates of the entire field’s conditions. The estimated field conditions could then be fed back to an adaptive or self-learning control system. The self-learning control system would then calculate the best irrigation commands for the next few hours or day(s) based on a field model, the estimated field conditions, local weather forecast as well as other pre-specified irrigation requirements. Due to significant nonlinearities, uncertainties, and large scale of agricultural fields, there are great challenges in modeling, sensing, estimation and control algorithm development that need to be addressed. We have been actively developing agro- hydrological models, remote sensing and sensor fusing algorithms, optimal control algorithms to realize this revolutionary closed-loop smart irrigation vision in collaboration with different partners (including sensing instruments provider, sprinkler manufacturer, farmers, and government agency).