## 4 Discussion  
In the first experiment, the SMF model continuously forecasts the next seven sampled moisture levels and compared them with the practice sampled moisture levels. The measuring scale of the sensing components in this article is 0 to 1023. The forecasting error is less than 1.2% measuring scale. Using a small segment of the continuously sampled moisture records can help the SMF closely fit the soil moisture variation caused by environmental factors. In this experiment, by applying the moving average in statistics, the forecasting error on the moisture levels is 18 to 32. The experimental results are not better than the SMF model. From the results of Figure 8, the forecasting errors by using long irrigation intervals seem higher than that of the short ones. Forecasting the moisture levels in long irrigation intervals will suffer more influences from the environmental factors. So, the results are worse than the short irrigation intervals. In this work, the SMF model uses fix segment size of continuously sampled records to forecast the moisture. If the segment size can be dynamically adjusted by referring to the environmental factors, the results must be improved further. This will be the next research topic. In the second experiment, the CAIS watered the experimental plot from one point. This experiment just evaluated the 3 cm depth layer soil near the watering location. In this experiment, CAIS spends about 24 h to steady the soil moisture at the target level. The moisture levels, which are detected by the sensors placed at 2 cm, 3 cm, and 4 cm depth layer in VPA#4, increase and then become stable after CAIS adapts irrigation interval. The fluctuations of the moisture levels captured by the sensors placed at 2 cm and 4 cm depth layer of soil are larger than the sensors placed at the 3 cm depth layer because the irrigation interval adjustment is according to the 3 cm depth layer sensor. The moisture levels at different depth layers of soil also influence the moisture levels of 3 cm ones. For the neighboring VPA#0, the soil moisture does not retain at the target level explicitly. However, the containing water in the soil will also influence the moisture level of the VPA#4. Thus, the experiment with multiple irrigation points is necessary for proving the moisture-retaining ability of the CAIS. CAIS can only retain the constant soil moisture at a small area in the scenario that the amount of water is only 15 mL in each watering. Thus, the next experiment evaluates the case with multiple irrigation points. The testbed is divided into nine VPAs shown in Figure 2a. The moisture level of VPA#0, VPA#1, and VPA#4 are captured. The object is to maintain the moisture level at 590 for all 3 cm depth layer of soil. In Figure 10, each of these three VPAs uses different irrigation intervals to keep the soil moisture staying at the target level. The moisture levels in each VPA become steady after about 24 h. The time for the moisture becoming steady is on Day2 20:00. In these three VPAs, most of the applied irrigation intervals are 3 h and 4 h after the time Day 2 20:00. Using multiple irrigation points produces more water to the experimental plot than using a single irrigation point. Therefore, CAIS prolongs the irrigation interval for adjusting the moisture level to retain the moisture at the target level. The results also indicate that dividing the experimental plot into multiple areas can efficiently reduce the impacts of soil density on soil moisture. The irrigated water can be evenly distributed to the whole experimental plot. Currently, CAIS only consider the moisture at a specific soil depth layer. If the irrigation interval selection can consider different depth layers of soil, the moisture level must be more stable, and this is the future work of the CAIS. Table 3 gives the comparison results of the proposed CAIS with the existing products. The watering area of the Brass Impact Sprinkler [31] is about 78 m\({}^{2}\). It is triggered by the water pressure.