Research on the Detection Method of the Threshing Rate of Corn Ears Based on Machine Vision

Xinping Li (), Shendi Xu, Wantong Zhang, Junyi Wang, Yanan Li, Bin Peng and Ruizhe Sun
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Xinping Li: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China
Shendi Xu: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China
Wantong Zhang: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China
Junyi Wang: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China
Yanan Li: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China
Bin Peng: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China
Ruizhe Sun: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China

Agriculture, 2024, vol. 14, issue 7, 1-18

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 R 2 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 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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