# Machine-Vision-Based Algorithm for Blockage Recognition of Jittering Sieve in Corn Harvester
## 3 Experimental Results and Analysis  
This section provides the experimental results and analysis. To start, we introduce the source of experimental scenes. Figure 6 shows the threshing system and the cleaning apparatus that were utilized in this study. The corn ears introduced in Section 2.1 were first put into threshing system to obtain the mixtures of kernels and impurities. Then pure kernels were separate from impurities through the cleaning apparatus. The cleaning apparatus is the original model of the John Deere 3316 harvester, such that it could simulate actual harvesting. We considered three rotational speeds of the threshing system, 400 rpm, 500 rpm, and 600 rpm, as they are the common rotational speeds in practical. For each rotational speed, we randomly selected the point-in-time to halt the cleaning process to acquire 3 test scenes. Thus, totally 9 scene were used for experiments. Scenes 1-3, 4-6 and 7-9 were acquired with the rotational speed of 400 rpm, 500 rpm, and 600 rpm, respectively. These scenes contained different parts of the jittering sieve of the cleaning apparatus with blockages. Higher rotational speed was set with higher feed quantity. The increase of feed quantity improved the kernel throughput on the jittering sieve per unit time, leading to the aggravation of kernel blocking. The images were processed by using the Matlab software platform on a personal computer. For each scene, we first divided the sieve into sub-sieves by using Algorithm 1. The aspect ratio \(\mu\) was set to 5 due to the size of jittering sieve. Then, we recognized blockages by using Algorithm 2. The parameter \(\lambda\) was set to 4. The maximum number of iterative cycles of expanding operation was set to 3. The lower thresholds \(\theta_{r},\theta_{g},\theta_{b}\) were set to 230, 160, and 0, respectively (the data were 8-bit). The upper thresholds \(\theta_{r},\theta_{g},\theta_{b}\) were set to 255, 255 and 140, respectively. Finally, we computed the blocking ratios for each sub-sieve of each scene, and compared the results to the actual blocking levels. The original objective images of test scenes and their dividing results are shown in Figures 7-9. To verify the performance of division, we compare the numbers of recognized row edges and column edges to the real ones, which are summarized in Table 2. It can be noted that for all scenes, all row edges and column edges were successfully recognized, such that all sub-sieves were accurately divided. Figure 5: Evaluation of blocking level. (**a**) Original image. (**b**) Sieve division and blockage recognition. (**c**) Blocking ratio of each sub-sieve. Figure 6: Used threshing system and cleaning apparatus. Figure 10: Recognition results of scenes with rotational speed of 400 rpm. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Number of Row** & **Number of Recognized** & **Success Rate** & **Number of (\%)** & **Number of (\%)** & **Success Rate** (\%)** \\ \hline Scene 1 & 15 & 15 & 100.00 & 4 & 4 & 100 \\ Scene 2 & 15 & 15 & 100.00 & 4 & 4 & 100 \\ Scene 3 & 16 & 16 & 100.00 & 4 & 4 & 100 \\ Scene 4 & 16 & 16 & 100.00 & 4 & 4 & 100 \\ Scene 5 & 15 & 15 & 100.00 & 4 & 4 & 100 \\ Scene 6 & 15 & 15 & 100.00 & 4 & 4 & 100 \\ Scene 7 & 15 & 15 & 100.00 & 4 & 4 & 100 \\ Scene 8 & 15 & 15 & 100.00 & 4 & 4 & 100 \\ Scene 9 & 12 & 12 & 100.00 & 4 & 4 & 100 \\ Total & **134** & **134** & **100.00** & **36** & **36** & **100.00** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary on recognized row edges and column edges. Figure 9: Dividing results of scenes with rotational speed of 600 rpm. Next, we conducted the experiments on blocking level evaluation. Based on the sieve dividing results and the blockage recognizing results, we computed the blocking ratios of each sub-sieve by using the strategy given in Section 2.4. For readable, we presented the blocking ratio in different colors in Figures 13-15. It could be noted the blocking ratios are obvious higher at the sub-sieves that contains more kernels.