# Machine-Vision-Based Algorithm for Blockage Recognition of Jittering Sieve in Corn Harvester
###### Abstract  
Jittering sieve is a significant component of corn harvester, and it is used to separate kernels from impurities. The sieves may be blocked by kernels during the separating process, leading to the reduction of working performance. Unfortunately, the automatic recognition of blockage has not been studied yet. To address this issue, in this study we develop machine-vision-based algorithms to divide the jittering sieve into sub-sieves and to recognize kernel blockages. Additionally, we propose the metric to evaluate blocking level of each sub-sieve, aiming to provide the basis for automatic blockage clearing. The performance of the proposed algorithm is verified through simulation experiments on real images. The success ratio of edge determination reaches 100%. The mean cross-correlation coefficient of the blockage levels and the actual numbers of blocked kernels for all test scenes is 0.932. The results demonstrate the proposed algorithm can be used for accurate blockage recognition, and the proposed metric is appropriate for evaluating the blockage level.  
corn harvest; jittering sieve; machine vision; blockage recognition +
Footnote †: journal: Medical  
recognize the required information [13; 14]. The \"red, green, and blue\" (RGB) image is the typical form of used images [15; 16]. For corn seeding, [PERSON] et al. utilized the system based on the high-speed camera to measure the space of seeds, as well as the speed while the seeds are falling [17]. To acquire the relative positions of seed drills, [PERSON] et al. proposed an MV-based method and developed the corresponding hardware [18]. For detecting the working condition of seed-sowing devices, [PERSON] et al. proposed the MV-based recognition method, by which the breadth, the coordinates, and the spaces of seed arrays could be obtained [19]. For the field of corn harvesting, [PERSON] et al. proposed the algorithm to recognize crack corn ears by using the RGB images. Then the working parameters could be adjusted based on the detection of crack ears [20]. [PERSON] et al. developed an MV-based method for peeling damage recognition [21]. [PERSON] et al. extracted kernel images by using the on-board system to recognize damage kernels from whole kernels [22]. In summary, the MV has been successively applied in various fields of corn seeding and harvesting. However, to date, the automatic detection of blockage of jittering sieve has not been reported. This deficiency limits the development of automatic cleaning of blockage. This is the problem we aim to address in this study. Our main contributions can be summarized as three aspects. First, we propose the algorithm based on an RGB image to divided the jittering sieve into a set of areas of sub-sieves. Second, we propose the recognition algorithm for the kernels that are left behind on the sieves. Third, we introduce a metric to evaluate the blocking level for each sub-sieve, aiming to provide the basis for automatic blockage clearing.  
The remainder of this paper is summarized as follows--Section 2 introduces the used materials and the proposed method. Section 3 presents the experimental results and analysis. Section 4 discusses the limitations of this study and the future work. Section 5 concludes this paper. The notations and abbreviations used in the remainder of this paper are introduced in Table 1.