# Grain Sieve Loss Fuzzy Control System in Rice Combine Harvesters
## Material and Methods
### Grain Loss Control Strategy and Performance Checking  
Selecting the deviation of the monitored grain sieve loss and its deviation variation rate as input variables, a fuzzy model was developed to control the cleaning process. The grain sieve loss sampled frequency was 10 Hz. By analyzing the time series of the recorded grain sieve loss under different working conditions, the basic domain of grain sieve loss was obtained. The output variables were the controlled quantities obtained by applying fuzzy inference to the corresponding input variables. To check the control performance of the designed fuzzy cleaning controller, the fuzzy query tables for the working parameters were obtained separately according to the maximum membership principle. Then, the corresponding proportional coefficients were multiplied into the values in the fuzzy query tables to obtain the actual regulation. After converting the actual regulation into the 'if-else' control statements respectively, the experiment was carried out in the test bench to check the controller performance. Before the experiment, the threshed outputs container in the test bench was filled with 120-kg threshed outputs, and it was ensured that all of the threshed outputs could be fed into the cleaning system within 50 s, which took several trials. The control effect could be checked by comparing the monitored grain sieve loss variation. Airflow velocities at certain points inside the cleaning shoe were measured by self-heating hot-wire anemometry (VS110, Nanjing, Neng Zhao Technology Co., Ltd., Nanjing, China, with a scope of 0.5-50 m\(\cdot\)s\({}^{-1}\) and a resolution of 0.01 m\(\cdot\)s\({}^{-1}\)) to reflect the changing of the cleaning working parameters. The cleaning experiment was carried out under the condition that the controller was not being activated first, and the initial working parameters of the cleaning system were defined as the combination that had the worst cleaning performance. Then, about 10 s later, when the cleaning system was filled with threshed materials and the anemometer monitoring value was stable, the controller was activated. The corresponding sieve loss number was continuously recorded, which was beneficial to analyze the control effect afterwards. At last, collecting all of the cleaning residue on the oil-skin, the grains were obtained by removing the MOG through using a re-cleaner (Agriculex ASC-3 Seed Cleaner, Guelph, ON, Canada), and the grain sieve loss ratio could be obtained immediately. The grain sieve loss ratio was compared through utilizing the controller and the grain sieve loss ratio at the initial working parameters. The experimental process and location of the anemometers and grain sieve loss sensor in the cleaning shoe were as shown in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Measured Value** \\ \hline Plant height/mm & 750–850 \\ Ear height/mm & 150–170 \\ Stalk moisture content/\% & 58–67 \\ Grain moisture content/\% & 22–29 \\ Straw/grain ratio & 1.9–2.2 \\ One thousand grain weight/g & 31.2 \\ \hline \end{tabular}
\end{table}
Table 2: Basic characteristics of the rice used in the test.  
Figure 5: Cleaning material acquire process for testing the performance of the controller, 1. Grain sieve loss sensor, 2. Residual flow, 3. Anemometer.