Low-rank-constraint-based machine vision algorithm for chaffer-sieve-clogging recognition of corn harvester

Rongqiang Zhao, Jun Fu, Zhi Chen, Lei Tian, Luquan Ren

Research output: Contribution to journalArticlepeer-review


Although a chaffer sieve is used to separate impurities from kernels during corn harvesting, it is often clogged by impurities, and this has a negative impact on the separating performance. Accurate image recognition is the primary step in automatic working parameter adjustment that helps avoid clogging. Unfortunately, meshes of sieve underneath the impurities cannot be recognized using existing algorithms, and the clogging area of mesh and the impurity in background cannot be distinguished easily. To address this issue, a low-rank-constraint-based sieve clogging recognition (LSCR) algorithm is proposed in this study. Unlike existing algorithms, the position and shape of meshes are accurately estimated using the low-rank optimization strategy, and there is no need of training samples or complete information related to the mesh outline from the target images. The clogging areas are then determined based on the difference in relative reflectance. The experimental results demonstrate that the overall recognition accuracy in pixel level using the LSCR algorithm reaches 0.943 for the test scenes, which is significantly higher than that of the existing algorithms. LSCR can be potentially used for online chaffer-sieve-clogging detection in corn harvesters.

Original languageEnglish (US)
Article number107056
JournalComputers and Electronics in Agriculture
StatePublished - Jul 2022


  • Chaffer sieve
  • Corn harvesting
  • Image processing
  • Machine vision

ASJC Scopus subject areas

  • Forestry
  • Agronomy and Crop Science
  • Computer Science Applications
  • Horticulture


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