# Smart Irrigation Systems from Cyber-Physical Perspective: State of Art and Future Directions
## 3 Problem Space
### Environment Factors Dimension
#### 3.2.1 Field Condition  
Several key terms are used to describe a field condition:  
_Soil types_: The soil texture triangle, a fundamental concept in soil science, introduced the three primary types of isolated soils: sand, silt, and clay. Sand particles are typically the largest grains visible to the naked eye. It exhibits rapid water infiltration and drainage due to its inability to retain water. Clay (size is less than 0.002 mm) retains water efficiently but impedes water infiltration. Salt sits between sand and clay with a size in the range of 0.002-0.05 mm. According to the USDA (United States of Agriculture), the ideal soil, which favors nearby waters, consists of 20% clay, 40% sand, and 40% silt on a flat surface [88].  
_Soil texture:_ It refers to the ratio of sand, silt, and clay in a sample. Soil texture profoundly influences water retention and availability, directly impacting irrigation strategies. For example, [PERSON] et al. [89] investigated the correlation between soil texture and irrigation effectiveness. The team employed soil moisture and humidity sensors to collect data for analysis. The findings indicated that silt soil demonstrated superior water retention capabilities compared to peat soil, which is a type of organic mixture soil under similar conditions.  
_Other Terms:_ The field's topography means the direction of surface and underground water flows. Topography is an elevation measurement, and the slope is the ratio change in that elevation over a certain distance. A perfect topography in agriculture is a flat field. However, almost all fields have slopes, while water always runs off and collects in low-lying areas. Slopes and elevation variations can affect water distribution and drainage. For example, [PERSON] et al. [90] used a thermal camera to obtain RGB images of the surface of the ground to conduct the learning model training; then, they used the thermal pictures and the machine learning model to estimate the slope and flatness of the ground.  
An advanced irrigation management system is urgently needed to monitor water reserves, safeguard plants from overwatering, and detect leaks within the system.  
Sensors such as water meters and water flow sensors can be used to obtain information about water size areas and water leakage. For instance, [PERSON] et al. [31] proposed over 10 types of sensor data, including water meters and water flow sensors, to detect water leakage. [PERSON] et al. [59] used an ultrasonic sensor to monitor the water tank's level status and send alerts to users with water shortages or water exceeding. Likewise, [PERSON] et al. [91] implemented a prototype system for landslide detection. The designed system leveraged different sensors (inclination sensor, soil moisture sensor, and camera sensor) to collect data to edge nodes that evaluate the risk of landslide.  
Unmanned Aerial Systems (UASs) have emerged as a transformative technology for real-time crop monitoring and management. This shift is exemplified by seminal survey papers like [17; 18; 92]. [PERSON] et al. [93] demonstrated the potential of UASs for comprehensive data collection. The deployment of an autonomous unmanned helicopter equipped with hyperspectral, visible, and laser sensors yielded valuable insights into phenomena like landslides, flooding, erosion, and phenology. Also, [PERSON] et al. [94] explored using drones equipped with LoRa radios to assess the optimal drone-sensor proximity for effective data collection. In this scenario, sensors are programmed to record data in their memory when the drone flies over, subsequently transmitting these data. To maintain quality data collection, sensors need to be organized in a grid along a predefined route. This methodology, while promising, necessitates further exploration for scenarios involving diverse field shapes, varied drone flight plans, and alternative sensor deployments. Incorporating UASs marks an advancement in the smart agriculture system.