Machine Vision for the Automatic Measurement of Broken Grain Fraction 1

Georg, Hans; Guth, N.; Bockisch, Franz-Josef

doi:10.1016/s1474-6670(17)47174-6

Cited by 2 publications

(1 citation statement)

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“…In the literature, some researches can be found engaging in grain damage auto-detection technology [5][6][7][8][9][10] . Georg et al [8] developed an automatic measuring system for whole and broken kernels in which a digital image processing method was chosen as a sensor. Jahari et al [9] developed a machine vision system with a double lighting device that gives promising results to detect and measure undesirable objects and damaged grain in harvested grain paddy.…”

Section: Introductionmentioning

confidence: 99%

Real-time grain breakage sensing for rice combine harvesters using machine vision technology

Chen

Liao

Zou

et al. 2020

International Journal of Agricultural and Biological Engineering

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Breakage rate is one of the most important indicators to evaluate the harvesting performance of a combine harvester. It is affected by operating parameters of a combine such as feeding rate, the peripheral speed of the threshing cylinder and concave clearance, and shows complex non-linear law. Real-time acquisition of the breakage rate is an effective way to find the correlation of them. In addition, real-time monitoring of the breakage rate can help the driver optimize and adjust the operating parameters of a combine harvester to avoid the breakage rate exceeding the standard. In this study, a real-time monitoring method for the grain breakage rate of the rice combine harvester based on machine vision was proposed. The structure of the sampling device was designed to obtain rice kernel images of high quality in the harvesting process. According to the working characteristics of the combine, the illumination and installation of the light source were optimized, and the lateral lighting system was constructed. A two-step method of "color training-verification" was applied to identify the whole and broken kernels. In the first step, the local threshold algorithm was used to get the edge of kernel particles in a few training images with binary transformation, extract the color spectrum of each particle in color-space HSL and output the recognition model file. The second step was to verify the recognition accuracy and the breakage rate monitoring accuracy through grabbing and processing images in the laboratory. The experiments of about 2300 particles showed that the recognition accuracy of 96% was attained, and the monitoring values of breakage rate and the true artificial monitoring values had good trend consistency. The monitoring device of grain breakage rate based on machine vision can provide technical supports for the intellectualization of combine harvester.

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