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Agriculture
Volume 14
Issue 7
10.3390/agriculture14071037
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Open AccessArticle
by Xinping Li SciProfiles Scilit Preprints.org Google Scholar Dr. Xinping Li is a Professor, a Doctoral Supervisor, and the Vice Dean of the College of Equipment[...] Read more Shendi Xu SciProfiles Scilit Preprints.org Google Scholar Wantong Zhang SciProfiles Scilit Preprints.org Google Scholar Junyi Wang SciProfiles Scilit Preprints.org Google Scholar Yanan Li SciProfiles Scilit Preprints.org Google Scholar Bin Peng SciProfiles Scilit Preprints.org Google Scholar Ruizhe Sun SciProfiles Scilit Preprints.org Google Scholar 
Xinping LiShendi XuWantong ZhangJunyi WangYanan LiBin PengRuizhe Sun
College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China
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Agriculture 2024, 14(7), 1037; https://doi.org/10.3390/agriculture14071037 (registeringDOI)
Submission received: 7 June 2024 / Revised: 20 June 2024 / Accepted: 27 June 2024 / Published: 28 June 2024
(This article belongs to the Section Agricultural Technology)
Abstract
The threshing rate is one of the important indexes to evaluate the effect of corn threshing. The weighing method is often used to calculate the depuration rate of maize at present. This method is time-consuming and laborious and can only calculate the overall threshing rate but does not give the threshing rate of individual corn ears. Different parameters of corn ears have complex effects on the threshing rate. By analyzing the threshing rate of each corn ear, we can choose the appropriate ear treatment method, optimize the processing equipment and process flow, and improve the threshing performance. This paper presents a method based on machine vision to detect the threshing rate of corn ears. In this method, machine vision was used to measure the parameters of the corncob and the area of the top of residual kernels. The area of the top of all kernels was restored based on the parameters of the corncob. The threshing rate of corn ears was calculated by the ratio of the area of the top of the missing kernel to the area of the top of all kernels after threshing. A bivariate linear regression area model was established to restore the area of the top of all corn kernels based on corncob parameters. The R2 was more significant than 0.98, and the goodness of fit was good. The machine vision inspection results showed that the maximum relative error of length and midsection radius was 7.46% and 5.55%, and the mean relative error was 2.58% and 2.23%. The maximum relative error of the corn ear threshing rate was 7.08%, and the mean relative error was 2.04%. When the residual kernels were concentrated in the midsection, the inspection result of the corn ear threshing rate was better. The maximum relative error was 3.98%, and the mean relative error was 1.07%. This paper provides a new idea and reference for measuring the threshing rate of corn ears.
Keywords: threshing rate of corn ears; parameters of the corncob; area of the top of corn kernels; machine vision
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MDPI and ACS Style
Li, X.; Xu, S.; Zhang, W.; Wang, J.; Li, Y.; Peng, B.; Sun, R. Research on the Detection Method of the Threshing Rate of Corn Ears Based on Machine Vision. Agriculture 2024, 14, 1037. https://doi.org/10.3390/agriculture14071037
AMA Style
Li X, Xu S, Zhang W, Wang J, Li Y, Peng B, Sun R. Research on the Detection Method of the Threshing Rate of Corn Ears Based on Machine Vision. Agriculture. 2024; 14(7):1037. https://doi.org/10.3390/agriculture14071037
Chicago/Turabian Style
Li, Xinping, Shendi Xu, Wantong Zhang, Junyi Wang, Yanan Li, Bin Peng, and Ruizhe Sun. 2024. "Research on the Detection Method of the Threshing Rate of Corn Ears Based on Machine Vision" Agriculture 14, no. 7: 1037. https://doi.org/10.3390/agriculture14071037
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
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MDPI and ACS Style
Li, X.; Xu, S.; Zhang, W.; Wang, J.; Li, Y.; Peng, B.; Sun, R. Research on the Detection Method of the Threshing Rate of Corn Ears Based on Machine Vision. Agriculture 2024, 14, 1037. https://doi.org/10.3390/agriculture14071037
AMA Style
Li X, Xu S, Zhang W, Wang J, Li Y, Peng B, Sun R. Research on the Detection Method of the Threshing Rate of Corn Ears Based on Machine Vision. Agriculture. 2024; 14(7):1037. https://doi.org/10.3390/agriculture14071037
Chicago/Turabian Style
Li, Xinping, Shendi Xu, Wantong Zhang, Junyi Wang, Yanan Li, Bin Peng, and Ruizhe Sun. 2024. "Research on the Detection Method of the Threshing Rate of Corn Ears Based on Machine Vision" Agriculture 14, no. 7: 1037. https://doi.org/10.3390/agriculture14071037
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
Agriculture, EISSN 2077-0472, Published by MDPI
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