## 3 Results
### Experiment 1: Evaluation of SMF Model
#### 3.2.1 SMF Model Deduction  
Figure 4a shows the layout of sensors for the SMF model. The probes were placed either 3 cm and 6 cm from the irrigation point to measure water diffusion under the soil. They did not touch the irrigated water directly. They captured soil moisture data at depths of 2 cm, 3 cm, and 4 cm at the sensor locations. Figure 4: Data gathering scenario for building the SMF model. The locations {a, b, c} and {d, e, f} are 3 cm and 6 cm to the irrigating point, respectively. The dimension of moisture levels is 0–1023. (**a**) The soil sensors for collecting data in the SMF model are installed around the watering point with three directions. The distances of the sensor in different directions are the same. (**b**) The moisture variation of the six installed sensors with different depth layers. The moisture values of the sensors located at 6 cm to the watering point do not have an explicit reaction. Figure 4b shows the moisture data sampled by the six sensors. In this experiment, 15 mL of irrigation water was applied every 2 h. The sensors were calibrated each time they were plugged into the soil. The comparator chip was an LM393 where the output voltage changed according to the water content in the soil. The output voltage decreased if the soil was wet, and increased if the soil was dry. To provide a more intuitive view of the moisture data, the output values were inversed, so a high value indicated high moisture, and a low value indicated low moisture. Three bottles of soil were prepared for calibration. The first one was dry soil, which was almost a powder. To guarantee that the dry soil contained no water, the soil was placed into a transparent PET (polyethylene terephthalate) bottle and the lid was tightly fastened. The bottle was placed in the sun to check there was no evaporation. The YL-69 voltage value of the dry soil was 0-2. The soil in the second bottle was the original cultivation soil that contained some water. The value captured by the YL-69 ranged from 568 to 570. The bottle was weighed when it was first filled with soil, then water was added if the weight fell. The third bottle mixed dry soil with pure water to completely saturate the soil. The value captured by the sensor was 1020. The sensor calibration first used the second bottle to adjust the output value of YL-69 to range from 568 to 570. Then, the third bottle was used to verify whether the value captured by the sensor ranged from 1020 to 1023. All the sensors sampled the moisture level in the soil every minute. The level of the sampled moisture ranged from 0 (completely dry) to 1023 (completely saturated). The results showed that the moisture values at the 3 cm locations [a, b, c] had a different reaction to the ones at the 6 cm locations {d, e, f} after watering. The moisture values at the locations {d, e, f} did not increase immediately but grew moderately over time. To observe the moisture diffusion under the soil, the experiment used locations 3 cm from the irrigating point to sample the moisture values. Figure 5a illustrates the moisture data captured by the sensors placed at the 4 cm depth layers of soil and Figure 5b,c show the corresponding values of air temperature and humidity. The irrigation interval in this experiment was 2 h. Sensors sampled the moisture values before irrigation. The total time of this experiment was 160 h. R language was used to compute the regression curve of these sampled values. The results indicated that there was high variability in the data, with many sampled values far from the regression curve. The closest regression function was a sixth-order polynomial function. This test showed that it was difficult to deduce a suitable prediction function from the total sampled moisture dataset because the environmental variability was too high. Figure 5: _Cont._  
Figure 5d shows multiple regression curves started from different points. Instead of using the entire sampled dataset, each curve used a small segment of continuously sampled moisture data to deduce the trend function.