# Smart Irrigation Systems from Cyber-Physical Perspective: State of Art and Future Directions
## 3 Problem Space
### Environment Factors Dimension
#### 3.2.2 Natural Variations  
Natural variations, encompassing weather patterns, temperature fluctuations, and precipitation levels over the year, play a significant role in irrigation scheduling and water demand estimation. The dynamic interplay between these factors and the chosen crop types affects water requirements. Particularly during dry periods, the necessity for increased irrigation becomes pronounced. Water reservoirs or storage tanks must be adequately provisioned to cater to these enhanced needs.  
In response to these natural variations, innovative irrigation systems have been developed. For example, [PERSON] et al. [59] devised a water discharge scheduling strategy based on diverse factors such as rainfall, hydrology, soil characteristics, plant species, and climatic data. Once the water reservoir reaches its maximum capacity, the system triggers alerts to halt irrigation. Conversely, users are notified to refill the reservoir if water levels drop below a designated minimum threshold. [PERSON] and [PERSON] et al. [40] proposed an intelligent farming system using weather forecasts and history data to estimate the water in need. It added a manual option to turn the irrigation system from automatic to manually controlled. During the rainy season, once the data meet the maximum level, the irrigation system is turned off until the user manually turns it on. Also, [PERSON] et al. [56]designed an irrigation system that automatically starts sucking out the excess water with the pump when the water level exceeds the ideal point for the crop.  
Maize is one essential grain crop in China. [PERSON] et al. [95] designed a machine learning-based model to predict soil moisture level at various depths with different locations for maize during the summer growth period. Likewise, [PERSON] et al. [79] used data collected by synthetic aperture radar (SAR) for winter wheat fields in Canada. The winter wheat region is generally sown in October and then starts its regrowth in April of the following year. Winter wheat harvest commonly happens near end of July or early August, depending on the area. The study recorded the soil moisture data through SAR during the whole growth period to develop several machine learning models. Based on their research, it is possible to estimate the soil moisture value with the machine learning model if the environmental conditions can be controlled.  
Interestingly, irrigation systems are not solely dedicated to water provision. They can also be harnessed to regulate soil temperature [31; 33; 43]. Soil temperature sensors can monitor thermal conditions. When temperatures rise excessively, the irrigation system can be triggered to mitigate the heat by supplying water, thereby preserving optimal growing conditions.  
Overall, accounting for natural variations is crucial in smart irrigation systems. Advances in data-driven techniques, sensor integration, and machine learning facilitate accurately estimating irrigation needs based on weather patterns, temperature changes, and crop types. Moreover, irrigation systems can serve multifunctional roles, such as soil temperature regulation.